Difference between revisions of "Literature"
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'''Automatic selection of GCC optimization options using a gene weighted genetic algorithm.''' | '''Automatic selection of GCC optimization options using a gene weighted genetic algorithm.''' | ||
:Lin, S., Chang, C., & Lin, N. (2008). ''Automatic selection of GCC optimization options using a gene weighted genetic algorithm.'' Computer Systems Architecture Conference, 2008. ACSAC 2008. 13th Asia-Pacific, 1–8. doi:10.1109/APCSAC.2008.4625477 | :Lin, S., Chang, C., & Lin, N. (2008). ''Automatic selection of GCC optimization options using a gene weighted genetic algorithm.'' Computer Systems Architecture Conference, 2008. ACSAC 2008. 13th Asia-Pacific, 1–8. doi:10.1109/APCSAC.2008.4625477 | ||
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| + | '''Meta optimization: Improving compiler heuristics with machine learning.''' | ||
| + | :Stephenson, M., & Amarasinghe, S. (2003). ''Meta optimization: Improving compiler heuristics with machine learning.'' Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation. Retrieved from http://dl.acm.org/citation.cfm?id=781141 | ||
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| + | '''Using Machine Learning to Automate Compiler Optimisation''' | ||
| + | :Thomson, J. D. (2008). Using Machine Learning to Automate Compiler Optimisation. | ||
==Feature Selection== | ==Feature Selection== | ||
Revision as of 13:02, 22 March 2013
This page lists literature and related work that may be relevant to the project.
Machine Learning
MILEPOST
- Fursin, G., Kashnikov, Y., Memon, A. W., Chamski, Z., Temam, O., Namolaru, M., Yom-Tov, E., et al. (2011). Milepost GCC: machine learning enabled self-tuning compiler. International Journal of Parallel Programming, 1–31.
Mitigating the compiler optimization phase-ordering problem using machine learning.
- Kulkarni, S., & Cavazos, J. (2012). Mitigating the compiler optimization phase-ordering problem using machine learning. Proceedings of the ACM international conference on Object oriented programming systems languages and applications, 1–16. Retrieved from http://dl.acm.org/citation.cfm?id=2384616.2384628
Rapidly Selecting Good Compiler Optimizations using Performance Counters.
- Cavazos, J., Fursin, G., Agakov, F., Bonilla, E., O’Boyle, M. F. P., & Temam, O. (2007). Rapidly Selecting Good Compiler Optimizations using Performance Counters. International Symposium on Code Generation and Optimization (CGO’07) (pp. 185–197). IEEE. doi:10.1109/CGO.2007.32
Automatic selection of GCC optimization options using a gene weighted genetic algorithm.
- Lin, S., Chang, C., & Lin, N. (2008). Automatic selection of GCC optimization options using a gene weighted genetic algorithm. Computer Systems Architecture Conference, 2008. ACSAC 2008. 13th Asia-Pacific, 1–8. doi:10.1109/APCSAC.2008.4625477
Meta optimization: Improving compiler heuristics with machine learning.
- Stephenson, M., & Amarasinghe, S. (2003). Meta optimization: Improving compiler heuristics with machine learning. Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation. Retrieved from http://dl.acm.org/citation.cfm?id=781141
Using Machine Learning to Automate Compiler Optimisation
- Thomson, J. D. (2008). Using Machine Learning to Automate Compiler Optimisation.
Feature Selection
MILEPOST Feature list [1]
Spatial Based Feature Generation for Machine Learning Based Optimization Compilation.
- Malik, A. M. (2010). Spatial Based Feature Generation for Machine Learning Based Optimization Compilation. 2010 Ninth International Conference on Machine Learning and Applications, 925–930. doi:10.1109/ICMLA.2010.147
