Graph Neural Network-Based Program Optimizer

Abstract

This paper introduces a novel approach to program optimization leveraging the power of Graph Neural Networks (GNNs). The core idea is to represent program abstractions, such as Control Flow Graphs (CFGs) and Data Dependency Graphs (DDGs), as graphs and utilize GNNs to automatically identify optimization opportunities and generate optimized code. Traditional program optimization techniques often rely on hand-crafted heuristics and domain-specific knowledge. This approach offers a more automated and potentially more effective method by allowing the GNN to learn complex optimization patterns directly from the program's structure. We demonstrate the feasibility and potential of this approach through theoretical analysis and a conceptual framework. The key contribution lies in the application of GNNs to program optimization, providing a fundamentally new paradigm for achieving better code optimization. ---