Abstract: Traditional compiler optimizations face challenges such as rigid rules,limited adaptability across scenarios,and the efficiency ceiling imposed by manual design,making it difficult to meet the diverse demands of efficient compilation for different programs.To address this,researchers have proposed algorithms such as auto-tuning and Bayesian optimization to search for optimal optimization flags and parameters.However,existing methods suffer from two major drawbacks.Firstly,they require iterative execution of the target code,which is computationally expensive and infeasible for large-scale programs.Secondly,they treat programs as black boxes,leading to dependence on initial configurations,susceptibility to local optima,and the need for re-optimization when applied to new programs.To overcome these limitations,this paper proposes an intelligent compiler optimization technique based on program features.The approach involves collecting program feature information through the compiler and training a deep learning model on a dataset containing both program features and high-performing optimization flag combinations,enabling the model to predict suitable optimization options based on input program characteristics.Furthermore,for optimization processes such as loop unrolling,fine-grained parameter tuning is achieved by combining machine learning models with program features to enable intelligent selection of key parameters.Evaluated on the standard-scale SPEC CPU2017 integer benchmark suite,the proposed method achieves a 7.2% performance improvement over the O3 optimization level,with an average model inference time of only 7.11 seconds and a feature extraction overhead of just 1.02% of total compilation time.Experimental results demonstrate that the technique can effectively predict appropriate optimization flags and parameters for different programs,offering significant cost advantages over existing approaches.

Key words: Compiler optimization, Program features, Learning-based optimization, Compiler configuration selection, Performance prediction