##基于图神经网络的编译器优化

Abstract

This paper proposes a novel approach to compiler optimization by leveraging the power of Graph Neural Networks (GNNs). Traditional compiler optimization techniques rely heavily on hand-crafted heuristics and static analysis, often struggling to capture complex code relationships and achieve optimal performance. We introduce a framework where compiler optimization is framed as a learning task within a GNN. Code is represented as a graph, with nodes representing individual code elements (e.g., instructions, variables) and edges representing dependencies between them. The GNN learns to propagate information across this graph, effectively capturing the intricate dependencies and potential optimization opportunities within the code. The learned representations are then utilized to guide optimization decisions, leading to improved optimization performance. This work demonstrates a promising new direction for compiler optimization, offering a more intelligent and potentially more efficient method compared to traditional approaches.