The Twisted N-Cube with Application to Multiprocessing
IEEE Transactions on Computers
On Self-Diagnosable Multiprocessor Systems: Diagnosis by the Comparison Approach
IEEE Transactions on Computers
A new variation on hypercubes with smaller diameter
Information Processing Letters
A Graph Partitioning Approach to Sequential Diagnosis
IEEE Transactions on Computers
Better Adaptive Diagnosis of Hypercubes
IEEE Transactions on Computers
IEEE Transactions on Computers
A comparison connection assignment for diagnosis of multiprocessor systems
ISCA '80 Proceedings of the 7th annual symposium on Computer Architecture
The Connection Machine
A fast pessimistic one-step diagnosis algorithm for hypercube multicomputer systems
Journal of Parallel and Distributed Computing
Diagnosabilities of Regular Networks
IEEE Transactions on Parallel and Distributed Systems
Independent spanning trees vs. edge-disjoint spanning trees in locally twisted cubes
Information Processing Letters
A survey of comparison-based system-level diagnosis
ACM Computing Surveys (CSUR)
Fault-tolerant edge-pancyclicity of locally twisted cubes
Information Sciences: an International Journal
Note: Embedding two edge-disjoint Hamiltonian cycles into locally twisted cubes
Theoretical Computer Science
Fault diagnosis for hypercube-like networks
AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
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Comparison-based diagnosis is a practical approach to the system-level fault diagnosis of multiprocessors. The locally twisted cube is a newly introduced hypercube variant, which not only possesses lower diameter and better graph embedding capability as compared with a hypercube of the same size, but retains some nice properties of hypercubes. This paper addresses the fault diagnosis of locally twisted cubes under the MM^* comparison model. By utilizing the existence of abundant cycles within a locally twisted cube, we present a new diagnosis algorithm. With elaborately organized data, this algorithm can run in O(Nlog"2^2N) time, where N stands for the total number of nodes. In comparison, the classical Sengupta-Dahbura diagnosis algorithm takes as much as O(N^5) time to achieve the same goal. As a consequence, the proposed algorithm is remarkably superior to the Sengupta-Dahbura algorithm in terms of the time overhead.