System-level fault identification is a key subject for maintaining the reliability of multiprocessor interconnected systems. This task requires fast and accurate inferences based on big volume of data, and the problem...System-level fault identification is a key subject for maintaining the reliability of multiprocessor interconnected systems. This task requires fast and accurate inferences based on big volume of data, and the problem of fault identification in an unstructured graph has been proved to be NP-hard （non-deterministic polynomial-time hard）. In this paper, we adopt the PMC diagnostic model （first proposed by Preparata, Metze, and Chien） as the foundation of point-to-point probing technology, and a system contains only restricted-faults if every of its fault-free units has at least one fault-free neighbor. Under this condition we propose an efficient method of identifying restricted-faults in the folded hypercube, which is a promising alternative to the popular hypercube topology.展开更多
With the development of high-performance computing and the expansion of large-scale multiprocessor sys-tems,it is significant to study the reliability of systems.Probabilistic fault diagnosis is of practical value to ...With the development of high-performance computing and the expansion of large-scale multiprocessor sys-tems,it is significant to study the reliability of systems.Probabilistic fault diagnosis is of practical value to the reliabilityanalysis of multiprocessor systems.In this paper,we design a linear time diagnosis algorithm with the multiprocessor sys-tem whose threshold is set to 3,where the probability that any node is correctly diagnosed in the discrete state can be cal-culated.Furthermore,we give the probabilities that all nodes of a d-regular and d-connected graph can be correctly diag-nosed in the continuous state under the Weibull fault distribution and the Chi-square fault distribution.We prove thatthey approach to 1,which implies that our diagnosis algorithm can correctly diagnose almost all nodes of the graph.展开更多
基金supported in part by the NSC under Grand No.NSC102-2221-E-468-018
文摘System-level fault identification is a key subject for maintaining the reliability of multiprocessor interconnected systems. This task requires fast and accurate inferences based on big volume of data, and the problem of fault identification in an unstructured graph has been proved to be NP-hard （non-deterministic polynomial-time hard）. In this paper, we adopt the PMC diagnostic model （first proposed by Preparata, Metze, and Chien） as the foundation of point-to-point probing technology, and a system contains only restricted-faults if every of its fault-free units has at least one fault-free neighbor. Under this condition we propose an efficient method of identifying restricted-faults in the folded hypercube, which is a promising alternative to the popular hypercube topology.
基金supported by the National Natural Science Foundation of China under Grant Nos.62172291,62272333,and U1905211the Postgraduate Research and Practice Innovation Program of Jiangsu Province of China under Grant No.KYCX21_2961+1 种基金Jiangsu Province Department of Education Future Network Research Fund Project under Grant No.FNSRFP-2021YB-39the Priority Academic Program Development of Jiangsu Higher Education Institutions,and the Collaborative Innovation Center of Novel Software Technology and Industrialization.
文摘With the development of high-performance computing and the expansion of large-scale multiprocessor sys-tems,it is significant to study the reliability of systems.Probabilistic fault diagnosis is of practical value to the reliabilityanalysis of multiprocessor systems.In this paper,we design a linear time diagnosis algorithm with the multiprocessor sys-tem whose threshold is set to 3,where the probability that any node is correctly diagnosed in the discrete state can be cal-culated.Furthermore,we give the probabilities that all nodes of a d-regular and d-connected graph can be correctly diag-nosed in the continuous state under the Weibull fault distribution and the Chi-square fault distribution.We prove thatthey approach to 1,which implies that our diagnosis algorithm can correctly diagnose almost all nodes of the graph.
