摘要
Modern compilers apply various code transformation algorithms to improve the quality of the target code. However, a complex problem is to determine which transformation algorithms must be utilized. This is difficult because of three reasons： a number of transformation algorithms, various combination possibilities, and several configuration possibil- ities. Over the last few years, various intelligent systems were presented in the literature. The goal of these systems is to search for transformation algorithms and thus apply them to a certain program. This paper proposes a flexible, low-cost and intelligent system capable of identifying transformation algorithms for an input program, considering the program＇s specific features. This system is flexible for parameterization selection and has a low-computational cost. In addition, it has the capability to maximize the exploration of available computational resources. The system was implemented under the Low Level Virtual Machine infrastructure and the results indicate that it is capable of exceeding, up to 21.36%, performance reached by other systems. In addition, it achieved an average improvement of up to 17.72% over the most aggressive compiler optimization level of the Low Level Virtual Machine infrastructure.
Modern compilers apply various code transformation algorithms to improve the quality of the target code. However, a complex problem is to determine which transformation algorithms must be utilized. This is difficult because of three reasons： a number of transformation algorithms, various combination possibilities, and several configuration possibil- ities. Over the last few years, various intelligent systems were presented in the literature. The goal of these systems is to search for transformation algorithms and thus apply them to a certain program. This paper proposes a flexible, low-cost and intelligent system capable of identifying transformation algorithms for an input program, considering the program＇s specific features. This system is flexible for parameterization selection and has a low-computational cost. In addition, it has the capability to maximize the exploration of available computational resources. The system was implemented under the Low Level Virtual Machine infrastructure and the results indicate that it is capable of exceeding, up to 21.36%, performance reached by other systems. In addition, it achieved an average improvement of up to 17.72% over the most aggressive compiler optimization level of the Low Level Virtual Machine infrastructure.
