基于图神经网络的编译器优化决策建模

Abstract

This paper introduces a novel approach to compiler optimization decision modeling by leveraging Graph Neural Networks (GNNs). Traditional compiler optimization techniques rely heavily on manually crafted rules and heuristics, often proving inadequate for complex codebases and emerging hardware architectures. This research proposes a probabilistic model built upon GNNs to represent and predict optimal optimization decisions. The model utilizes graph structures to encapsulate the intricate relationships within the compiler optimization process, with nodes representing optimization opportunities and edges signifying dependencies between them. Through neural network learning, the GNN dynamically adapts to the specific characteristics of the code, leading to more intelligent and effective optimization strategies. This work demonstrates the potential of GNNs to overcome the limitations of traditional methods and pave the way for automated, data-driven compiler optimization. The core claim is to utilize GNNs to build a probabilistic model for compiler optimization decisions, achieving intelligent optimization. The core mechanism involves modeling the compiler optimization process as a GNN, using graph nodes and edges to represent optimization factors and enabling learning and prediction through neural networks.