Difference between revisions of "Literature"
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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__ |
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| + | ==Optimisation Selection and Ordering== | ||
| + | |||
| + | '''An approach to ordering optimizing transformations''' | ||
| + | :Whitfield, D., & Soffa, M. Lou. (1990). ''An approach to ordering optimizing transformations.'' ACM SIGPLAN Notices, 25(3), 137–146. doi:10.1145/99164.99179 | ||
| + | |||
| + | ==Machine Learning== | ||
'''MILEPOST''' | '''MILEPOST''' | ||
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: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 | ||
| − | ==Feature Selection== | + | |
| + | '''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=== | ||
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] | ||
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'''Spatial Based Feature Generation for Machine Learning Based Optimization Compilation.''' | '''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 | :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.'' | ||
| + | |||
| + | '''Towards energy-aware compilation + optimizations''' | ||
| + | :Zendra, O. (2013) ''Towards energy-aware compilation + optimizations'' GDR-GPL/Compil. URL: http://compilation.gforge.inria.fr/2013_04_Nancy/20130405_OZ_GDR-GPL_Compil.pdf | ||
| + | |||
| + | ==Benchmarking== | ||
| + | '''MiBench: A free, commercially representative embedded benchmark suite.''' | ||
| + | :Guthaus, M. R., & Ringenberg, J. S. (2001). ''MiBench: A free, commercially representative embedded benchmark suite.'' In IEEE International Workshop on Workload Characterization (WWC-4) (pp. 3–14). Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=990739 | ||
Latest revision as of 12:40, 17 August 2013
This page lists literature and related work that may be relevant to the project.
Contents
Optimisation Selection and Ordering
An approach to ordering optimizing transformations
- Whitfield, D., & Soffa, M. Lou. (1990). An approach to ordering optimizing transformations. ACM SIGPLAN Notices, 25(3), 137–146. doi:10.1145/99164.99179
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.
Towards energy-aware compilation + optimizations
- Zendra, O. (2013) Towards energy-aware compilation + optimizations GDR-GPL/Compil. URL: http://compilation.gforge.inria.fr/2013_04_Nancy/20130405_OZ_GDR-GPL_Compil.pdf
Benchmarking
MiBench: A free, commercially representative embedded benchmark suite.
- Guthaus, M. R., & Ringenberg, J. S. (2001). MiBench: A free, commercially representative embedded benchmark suite. In IEEE International Workshop on Workload Characterization (WWC-4) (pp. 3–14). Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=990739
