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Reliability evaluation for multi-graph matching composition networks
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AI
DOI：10.1080/02533839.2025.2565361.png)
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Abstract
En 中文
With the rapid development of high-performance computing, the demand for interconnection network (IN) performance is also increasing. Reliability is an important indicator for evaluating the nature of the IN. An IN with good performance (e.g. with maximal connectivity, maximal diagnosability, low diameter) can be designed to greatly improve information transmission and reduce IN costs. In this paper, we propose a multi-graph matching composition network (MMCN). Furthermore, several important parameters of MMCNs including connectivity, diagnosability, and diameter are characterized. Specifically, we prove the connectivity, diagnosability, upper bound of diameter, 1-extra connectivity, 2-extra connectivity, 1-extra conditional diagnosability, and 2-extra conditional diagnosability of MMCNs. Finally, we apply our results to a number of well-known INs including star graphs, pancake graphs, and some results for unknown INs.
Keywords:
Connectivity
diagnosability
PMC model
MM* model
Journal
J
IF:
1.2
Papers:
155
Citations:
1.1K
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