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Control flow graph based code optimization using graph neural networks
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DOI：10.3389/frobt.2026.1731740.png)
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Abstract
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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.
Keywords:
graph neural networks
code optimization
GCC
compilers
FLAG
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