Author:
Ni Youcong,Du Xin,Yuan Yuan,Xiao Ruliang,Chen Gaolin
Publisher
Springer Science and Business Media LLC
Reference39 articles.
1. Agakov, F., Bonilla, E., Cavazos, J., Franke, B., Fursin, G., O’Boyle, M.F., Thomson, J., Toussaint, M., Williams, C.K.: Using machine learning to focus iterative optimization. In: International Symposium on Code Generation and Optimization (CGO’06), pp. 11–pp. IEEE (2006)
2. Asher, Y.B., Haber, G., Stein, E.: A study of conflicting pairs of compiler optimizations. In: 2017 IEEE 11th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), pp. 52–58. IEEE (2017)
3. Ashouri, A..H., Killian, W., Cavazos, e.a: A survey on compiler autotuning using machine learning. ACM Comput. Surv. (CSUR) 51(5), 1–42 (2018)
4. Ashouri, A.H., Mariani, G., Palermo, G., Park, E., Cavazos, J., Silvano, C.: Cobayn: compiler autotuning framework using Bayesian networks. ACM Trans. Arch. Code Optim. (TACO) 13(2), 1–25 (2016)
5. Ashouri A.H., P.G.C.J.S.C.: Selecting the best compiler optimizations: A bayesian network approach. In: Automatic Tuning of Compilers Using Machine Learning, pp. 41–70. Springer (2017)