Adaptive system-level fault diagnosis of hierarchical cubic networks
摘要
As the demand for computational power continues to rise, large-scale hierarchical networks play an increasingly pivotal role in high-performance computing (HPC) systems. Fault diagnosis is essential for ensuring the stability and reliability of these complex networks. However, traditional fault diagnosis methods face significant challenges in scalability and real-time performance as system sizes and complexities grow. This paper introduces a parallel adaptive system-level fault diagnosis algorithm (PAD-HCN) that leverages the Hamiltonian structure to efficiently and accurately diagnose faults in large-scale hierarchical cubic networks (HCNs). Specially, we first prove that HCNs are Hamiltonian, a property that forms the theoretical foundation for designing an efficient fault diagnosis mechanism. Building upon this property, we propose a parallel and adaptive fault diagnosis algorithm that significantly enhances diagnostic performance. Simulation results demonstrate that the proposed algorithm achieves exceptional diagnostic accuracy, maintaining nearly