Difference between revisions of "Literature"
From MAGEEC
(Change heading depth) |
|||
| Line 1: | Line 1: | ||
This page lists literature and related work that may be relevant to the project. | This page lists literature and related work that may be relevant to the project. | ||
| − | =Machine Learning= | + | __TOC__ |
| + | |||
| + | ==Machine Learning== | ||
'''MILEPOST''' | '''MILEPOST''' | ||
| Line 26: | Line 28: | ||
:Thomson, J. D. (2008). Using Machine Learning to Automate Compiler Optimisation. | :Thomson, J. D. (2008). Using Machine Learning to Automate Compiler Optimisation. | ||
| − | ==Feature Selection== | + | ===Feature Selection=== |
MILEPOST Feature list [http://ctuning.org/wiki/index.php/CTools:MilepostGCC:StaticFeatures:MILEPOST_V2.1] | MILEPOST Feature list [http://ctuning.org/wiki/index.php/CTools:MilepostGCC:StaticFeatures:MILEPOST_V2.1] | ||
| Line 34: | Line 36: | ||
: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 | :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 | ||
| − | =Energy Optimisation Theory= | + | ==Energy Optimisation Theory== |
'''Instruction scheduling based on energy and performance constraints.''' | '''Instruction scheduling based on energy and performance constraints.''' | ||
Revision as of 16:21, 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
Energy Optimisation Theory
Instruction scheduling based on energy and performance constraints.
- Parikh, A., Kandemir, M., Vijaykrishnan, N., & Irwin, M. J. (2000). Instruction scheduling based on energy and performance constraints. 2000 Proceedings. IEEE Computer Society Workshop on VLSI (pp. 37–42). IEEE Comput. Soc. doi:10.1109/IWV.2000.844527
Improving Energy Consumption by Compiler Optimization Technique Register Pipelining
- Steinke, S., & Schwarz, R. (n.d.). Improving Energy Consumption by Compiler Optimization Technique Register Pipelining.
