Control flow graph based code optimization using graph neural networks

Official URL: https://dx.doi.org/10.3389/frobt.2026.1731740

Selecting a good set of optimization flags requires extensive effort and expert input. While most of the prior research considers using static, spatial, or dynamic features, some of the latest research directly applied deep neural networks to source code. We combined the static features, spatial features, and deep neural networks by representing source code as graphs and trained Graph Neural Network for automatically finding suitable optimization flags. We created a dataset of 12000 graphs using 256 optimization flag combinations on 47 benchmarks. We trained and tested our model using these benchmarks, and our results show that we can achieve a maximum of 48.6% speed-up compared to the case where all optimization flags are enabled.