Literature

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

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