Generating new general compiler optimization settings

ABSTRACT

Finding nearly optimal optimization settings for modern compilers which can utilize a large number of optimizations is a combinatorially exponential problem. In this paper, we investigate whether in the presence of many optimization choices random generation of compiler settings can be used to obtain well performing compiler settings. We apply this random generation of compiler setting to gcc 3.3.1 which implements 54 optimizations. Our results show that this technique can be used to obtain a setting which exceeds the performance of the default optimization settings O1, O2, and O3 for each program in the SPECint95 benchmark suite. We also apply this technique to obtain a general setting which is suitable for many programs. This setting performs equally well as the default settings using significantly less options. Finally, we compare our setting with the default settings of gcc and analyze the difference.

REFERENCES

• 1: Alfred V. Aho , Ravi Sethi , Jeffrey D. Ullman, Compilers: principles, techniques, and tools, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1986
• 2: G. E. P. Box, W. G. Hunter, and J. S. Hunter. Statistics for Eperimenters. An Introduction to Design, Data Analysis, and Model Building. Wiley and Sons, 1978.
• 3: K. Chow and Y. Wu. Feedback-directed selection and characterization of compiler optimizations. In Proc. 2nd Workshop on Feedback Directed Optimization, 1999.
• 4: Keith D. Cooper , Philip J. Schielke , Devika Subramanian, Optimizing for reduced code space using genetic algorithms, Proceedings of the ACM SIGPLAN 1999 workshop on Languages, compilers, and tools for embedded systems, p.1-9, May 05-05, 1999, Atlanta, Georgia, United States
• 5: E. Granston and A. Holler. Automatic recommendation of compiler options. In Proc. 4th Workshop on Feedback-Directed and Dynamic Optimization, 2001.
• 6: T. Kisuki , P. M. W. Knijnenburg , M. F. P. O'Boyle, Combined Selection of Tile Sizes and Unroll Factors Using Iterative Compilation, Proceedings of the 2000 International Conference on Parallel Architectures and Compilation Techniques, p.237, October 15-19, 2000
• 7: Antoine Monsifrot , François Bodin , Rene Quiniou, A Machine Learning Approach to Automatic Production of Compiler Heuristics, Proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, p.41-50, September 04-06, 2002
• 8: Steven S. Muchnick, Advanced compiler design and implementation, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1998
• 9: A. Nisbet. GAPS: Genetic algorithm optimised parallelization. In Proc. Workshop on Profile and Feedback Directed Compilation, 1998.
• 10: GNU Consortium. GCC online documentation. http://www.gcc.gnu.org/onlinedocs/.
• 11: R. P. J. Pinkers , P. M. W. Knijnenburg , M. Haneda , H. A. G. Wijshoff, Statistical Selection of Compiler Options, Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS'04), p.494-501, October 04-08, 2004
• 12: Mark Stephenson , Saman Amarasinghe , Martin Martin , Una-May O'Reilly, Meta optimization: improving compiler heuristics with machine learning, Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation, June 09-11, 2003, San Diego, California, USA
• 13: Jean-Paul Tremblay , Paul G. Sorenson, Theory and Practice of Compiler Writing, McGraw-Hill, Inc., New York, NY, 1985
• 14: Spyridon Triantafyllis , Manish Vachharajani , Neil Vachharajani , David I. August, Compiler optimization-space exploration, Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization, March 23-26, 2003, San Francisco, California
• 15: W. M. Waite , G. Goos, Compiler Construction, Springer-Verlag New York, Inc., Secaucus, NJ, 1985
• 16: Deborah L. Whitfield , Mary Lou Soffa, An approach for exploring code improving transformations, ACM Transactions on Programming Languages and Systems (TOPLAS), v.19 n.6, p.1053-1084, Nov. 1997 [doi>10.1145/267959.267960]
• 17: Min Zhao , Bruce Childers , Mary Lou Soffa, Predicting the impact of optimizations for embedded systems, Proceedings of the 2003 ACM SIGPLAN conference on Language, compiler, and tool for embedded systems, June 11-13, 2003, San Diego, California, USA
• 18: Wankang Zhao , Baosheng Cai , David Whalley , Mark W. Bailey , Robert van Engelen , Xin Yuan , Jason D. Hiser , Jack W. Davidson , Kyle Gallivan , Douglas L. Jones, VISTA: a system for interactive code improvement, Proceedings of the joint conference on Languages, compilers and tools for embedded systems: software and compilers for embedded systems, June 19-21, 2002, Berlin, Germany

INDEX TERMS

Primary Classification:
  D. Software
  D.3 PROGRAMMING LANGUAGES
      D.3.4 Processors

General Terms:
Experimentation, Languages, Performance

Keywords:
compiler optimization, compiler tuning, iterative compilation