Machine Learning-Driven GCC Loop Unrolling Optimization: Compiler Performance Enhancement Strategy Based on XGBoost

In contemporary compilers, the determination of the loop unrolling factor is traditionally based on manually crafted heuristic rules. This approach heavily relies on human intuition, which limits its ability to achieve optimized performance across diverse architectures and can sometimes even lead to performance declines. Additionally, developers face challenges in achieving cross-platform compatibility, often necessitating extensive redesign efforts. In response, this study introduces a method leveraging the XGBoost algorithm to predict the optimal loop unrolling factor for compiler optimization, thereby aiming to replace human thinking with machine learning methods and standardize development processes. Initially, the study gathers data on the loop unrolling factors as determined by profile guided optimization technology, analyzes program-specific loop feature vectors and employs cross-validation, including the Pearson correlation coefficient and feature importance ranking, to construct a dataset. Subsequent use of XGBoost to train this dataset models the decision-making process for selecting the most effective loop unrolling factor. The final step involves integrating XGBoost’s trained decision tree model into GCC to calculate the optimal loop unrolling factor during actual compilation. Empirical results on the RISC-V platform indicate that this new method, when tested against the SPEC CPU 2006 benchmark suite, offers up to 6.18% improvement in performance over the existing heuristic approach. It provides a new method for loop unrolling in compilers, and provides an innovative guide for the application of machine learning in compilers.