A Novel Conditional Diagnostic Scheme for Hypercube-Based Multiprocessor Systems

With the scale of multiprocessor systems constantly increasing, the large number of interconnected processors (or nodes) makes faulty nodes inevitable. The fault diagnosis of multiprocessor systems therefore is a key technique for the system’s robustness. In this paper, we first propose a novel diagnostic metric, the h-extra r-component diagnosability, denoted ECD^h_r(G), which characterizes one special pattern of faults. We derive some theoretical results for the ECD of hypercube, denoted ECD^h_r(Q_n), under the PMC model. Diagnostic algorithms is proposed and implemented to detect faulty nodes that will disconnect hypercube Q_n into r components each containing at least h + 1 nodes. We also test the ECD-PMC algorithm to the hypercube network with different number of faulty processors satisfying the h-extra r-component condition. Extensive simulation results show that our proposed method achieves very good performance in terms of ACCR, TPR, FPR, and TNR.