Determining the optimal sequence of compiler optimization passes is challenging due to the extensive and intricate search space. Traditional auto-tuning techniques, such as iterative compilation and machine learning methods, are often limited by high computational costs and difficulties in generalizing to new programs. These approaches can be inefficient and may not fully address the varying optimization needs across different programs. This paper introduces a novel approach that leverages the synergistic relationships between optimization passes to effectively reduce the search space. By focusing on synergistic pass pairs that jointly optimize IR instruction counts, our method uses K-means clustering to capture common optimization patterns across programs and forms these pairs into coresets. Leveraging a supervised learning model trained on these coresets, we effectively predict the most beneficial coreset for new programs, streamlining the search for optimal sequences. By integrating various search strategies, our method quickly converges to near-optimal solutions.

Our approach achieves state-of-the-art performance on ten benchmark datasets, including MiBench, CBench, NPB, and CHStone, demonstrating an average reduction of 7.5% in IR instruction count compared to Oz. Furthermore, by conducting searches on the large set of synergistic pass pairs that we provided, our method achieves an average codesize reduction of 13.6% compared to Oz, outperforming existing search-based techniques across five datasets.