Tsoa: a two-stage optimization approach for GCC compilation options to minimize execution time

The open-source compiler GCC offers numerous options to improve execution time. Two categories of approaches, machine learning-based and design space exploration, have emerged for selecting the optimal set of options. However, they continue to face challenge in quickly obtaining high-quality solutions due to the large and discrete optimization space, time-consuming utility evaluation for selected options, and complex interactions among options. To address these challenges, we propose TSOA, a Two-Stage Optimization Approach for GCC compilation options to minimize execution time. In the first stage, we present OPPM, an Option Preselection algorithm based on Pattern Mining. OPPM generates diverse samples to cover a wide range of option interactions. It subsequently mines frequent options from both objective-improved and non-improved samples. The mining results are further validated using CRC codes to precisely preselect options and reduce the optimization space. Transitioning to the second stage, we present OSEA, an Option Selection Evolutionary optimization Algorithm. OSEA is grounded in solution preselection and an option interaction graph. The solution preselection employs a random forest to build a classifier, efficiently identifying promising solutions for the next-generation population and thereby reducing the time spent on utility evaluation. Simultaneously, the option interaction graph is built to capture option interplays and their influence on objectives from evaluated solutions. Then, high-quality solutions are generated based on the option interaction graph. We evaluate the performance of TSOA by comparing it with representative machine learning-based and design space exploration approaches across a diverse set of 20 problem instances from two benchmark platforms. Additionally, we validate the effectiveness of OPPM and conduct related ablation experiments. The experimental results show that TSOA outperforms state-of-the-art approaches significantly in both optimization time and solution quality. Moreover, OPPM outperforms other option preselection algorithms, while the effectiveness of random forest-assisted solution preselection, along with new solution generation based on the option interaction graph, has been verified.