Dynamic Topology-Dependent Graph Reasoning

Abstract

This paper proposes a novel approach to program optimization that leverages dynamic graph reasoning based on topology. Traditional static dependency graphs fail to adapt to runtime variations and resource constraints, leading to suboptimal performance. We introduce a system that utilizes a neural network to learn and represent program dependencies, and dynamically adjusts the topology of the dependency graph based on observed execution behavior. This is achieved through a reinforcement learning framework where the neural network learns to modify dependency weights, effectively creating a "living" graph that reflects the true runtime dependencies. The core claim is that this dynamic adaptation significantly improves compile-time resource allocation and runtime execution path selection. The key mechanism is the reinforcement learning trained neural network, which provides a continuous feedback loop for optimization. We present a detailed description of the system architecture and the reinforcement learning algorithm, emphasizing the adaptive nature of the dependency graph. Experimental results, although not presented here due to the focus on the theoretical framework, would demonstrate the potential benefits of this approach.