Compilation Optimization Pass Selection Using Gate Graph Attention Neural Network for Reliability Improvement

When dealing with different programs or applications, it is necessary to select the appropriate compilation optimization pass or subsequence for the program. Machine learning is widely used as an efficient technological means of solving this problem. However, the most important problem when using machine learning is the extraction of program features. Obtaining more semantic and syntax information and complex transitions among code segments from the source code are obviously necessary in this context, and is also an area that may have been neglected by previous work. Ensuring the integrity and effectiveness of program information is key to this problem. Moreover, when performing and improving the selection, the measurement indicators are often program performance, code size, etc.; there is limited research on program reliability in this context, which requires both the longest measurement time and the most complicated measurement methods. Accordingly, this paper establishes a combined program feature extraction model and proposes a graph-based compilation optimization pass selection model that learns heuristics for program reliability. This experiment was performed using the clang compilation framework. The alternative compilation optimization pass adopts the C language standard compilation optimization passes. Compared with traditional machine learning methods, our model improves the average accuracy by between 5% and 11% in the optimization pass selection for program reliability. Our experiments also demonstrate the strong scalability of our proposed model.