EAtuner: Comparative Study of Evolutionary Algorithms for Compiler Auto-tuning

Guojian Xiao, Siyuan Qin, Kuan Li, Juan Chen, Jianping Yin

Abstract: The manual adjustment of compilation flags by compiler users is impractical due to the exponential size of the search space. To address this, machine learning-based compiler auto-tuning methods, particularly evolutionary algorithms, have been proposed. However, existing works use different benchmarks and experimental setups, making it difficult to compare the strengths and weaknesses of various algorithms. To address this, we present EAtuner, an evolutionary algorithm-based framework for compiler auto-tuning, with the goal of benchmarking and identifying suitable algorithms for compiler auto-tuning. We implement ten discrete binary evolutionary algorithms and evaluate their effectiveness on the LLVM compiler through experiments. Notably, eight of these algorithms have not been previously applied to compiler flag optimization problems before our work. The results show that all ten algorithms can effectively achieve compiler auto-tuning, resulting in an average speedup of 1.204. However, there are notable differences in the effectiveness and efficiency of each algorithm, particularly in optimization efficiency, which is positively correlated with the number of program compilations. Based on this, we classify the algorithms into three levels, with Differential Evolution (DE) showing significant advantages in optimization effectiveness and efficiency. Additionally, we provide a comprehensive summary of the applicability of compiler flags, the correlation between them, and their relationship with programs.

External IDs:dblp:conf/cscwd/XiaoQL0Y24