swMATH ID: 28818
Software Authors: Ashouri Amir Hossein, Mariani Giovanni, Palermo Gianluca, Park Eunjung, Cavazos John, Silvano Cristina
Description: COBAYN: Compiler autotuning framework using Bayesian networks. This article proposes COBAYN: Compiler autotuning framework using BAYesian Networks, an approach for a compiler autotuning methodology using machine learning to speed up application performance and to reduce the cost of the compiler optimization phases. The proposed framework is based on the application characterization done dynamically by using independent microarchitecture features and Bayesian networks. The article also presents an evaluation based on using static analysis and hybrid feature collection approaches. In addition, the article compares Bayesian networks with respect to several state-of-the-art machine-learning models.
Homepage: https://dl.acm.org/citation.cfm?id=2928270
Source Code:  https://github.com/amirjamez/COBAYN
Related Software: TACT; Cole; Micomp; Acovea; LARA; Milepost GCC; LLVM; Nonio
Cited in: 0 Publications