Abstract
Modern compilers have many optimization passes which help to get a better binary code for a given program. These optimizations are NP-hard. People use different heuristics to get a near optimal solution. These heuristics are designed by a compiler expert after examining sample programs. This is a challenging task. Recently, people have used machine learning techniques instead of heuristics for compiler optimizations. Machine learning techniques have not only eliminated the human efforts but have also out-performed human made huristics. However, the human efforts have now been moved from creating heuristics to selecting good features. Selecting right set of features is important for machine learning techniques since no machine learning tool will work well with poorly choosen features. This paper introduces a noval approach to generate features for machine learning for compiler optimization problems with out any human involvement.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agakov, F., Bonilla, E., Cavazos, J., Franke, B., Fursin, G., O’Boyle, M., Thomson, J., Toussaint, M., Williams, C.: Using machine learning to focus iterative optimization. In: Proceedings of the International Symposium on Code Generation and Optimization, CGO 2006 (2006)
Bodin, F., Kisuki, T., Knijnenburg, P.M.W., O’Boyle, M., Rohou, E.: Iterative compilation in a non-linear optimization space. In: Workshop on Profile Directed Feedback-Compilation, PACT 1998 (1998)
Cavazos, J., O’Boyle, M.: Method-specific dynamic compilation using logistic regression. In: Proceedings of the 21st Annual ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages, and Applications, OOPSLA 2006 (2006)
Cavazos, J., Moss, J.: Inducing heuristics to decide whether to schedule. In: Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2004 (2004)
Cooper, K.D., Schielke, P.J., Subramanian, D.: Optimizing for Reduced Code Space using Genetic Algorithms. In: Workshop on Languages, Compilers, and Tools for Embedded Systems, LCTES 1999 (1999)
http://cis.temple.edu/~ingargio/cis587/readings/id3-c45.html
Dubach, C., Jones, T.M., Bonilla, E.V., Fursin, G., O’Boyle, M.F.: Portable Compiler optimization across embedded programs and micro-architectures using machine learning. In: Proceedings of the 42nd IEEE/ACM International Symposium on Micro-architecture (2009)
Fursin, G., Miranda, C., Temam, O., Namolaru, M., Yom-Tov, E., Zaks, A., Mendelson, B., Barnard, P., Ashton, E., Courtois, E., Bodin, F., Bonilla, E., Thomson, J., Leather, H., Williams, C., O’Boyle, M.: MILEPOST GCC: machine learning based research compiler. In: Proceedings of the GCC Developers’ Summit, GCC 2008 (2008)
Ganapathi, A., Datta, K., Fox, A., Patterson, D.: A case for machine learning to optimize multicore performance. In: Proceedings of the First USENIX Conference on Hot Topics in Parallelism, HotPar 2009 (2009)
Malik, A.M.: Spatial Based Feature Generation for Machine Learning Based Optimization Compilation. In: Proceedings of the 9th IEEE International Conference on Machine Learning and Applications, ICMLA 2010 (2010)
Mitchell, T.: Machine Learning. McGraw-Hill (1997)
McGovern, A., Moss, E.: Scheduling straight-line code using reinforcement learning and rollouts. In: Proceedings of Neural Information Processing Symposium, NIPS 1998 (1998)
Muchnick, S.: Compiler Optimization for Modern Compilers. Morgan Kaufmann (1997)
Ipek, E., Mckee, S.A.: Efficently exploring architectural design spaces via predictive modeling. In: Proceedings of Architectural Support for Programming Languages and Operating Systems, ASPLOS 2006 (2006)
Leather, H., Bonilla, E., O’Boyle, M.: Automatic feature generation for machine learning based optimizing compilation. In: Proceedings of the International Symposium on Code Generation and Optimization, CGO 2009 (2009)
Stephenson, M., Amarasinghe, S., Martin, M., O’Relly, U.M.: Meta optimization: Improving compiler heuristics with machine learning. In: Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2003 (2003)
Yuki, T., Renganarayanan, L., Rajopadhye, S., Anderson, C., Eichenberger, A., O’Brien, K.: Automatic Creation of Tile Size Selection Models. In: Proceedings of the International Symposium on Code Generation and Optimization, CGO 2010 (2010)
Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning, ICML 1997 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Malik, A.M. (2011). Automatic Static Feature Generation for Compiler Optimization Problems. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_78
Download citation
DOI: https://doi.org/10.1007/978-3-642-25832-9_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25831-2
Online ISBN: 978-3-642-25832-9
eBook Packages: Computer ScienceComputer Science (R0)