Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Graph Theory and Algorithms
Abstract
This paper introduces Dynamic Topology for Parallel Computation (DTPC), a novel framework for dynamically adjusting the topology of parallel computation graphs. Traditional parallel programming often relies on manual tuning, which can be time-consuming and suboptimal. DTPC leverages reinforcement learning to automatically optimize graph structure based on the characteristics of the computational task, offering a self-optimizing approach. The paper details the architecture, training process, and initial results demonstrating the effectiveness of this method in adapting to diverse workload characteristics. The core mechanism centers around a reinforcement learning agent that iteratively modifies graph edges and nodes to enhance performance. We present a comprehensive evaluation of DTPC on benchmark workloads, highlighting its ability to achieve significant improvements in throughput and latency compared to traditional approaches. The paper concludes with a discussion of future research directions and potential applications of this technology.
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