This paper is considered an important issue in the design of PE-Interconnection networks for massively parallel computing scalability. A detailed analysis shows that the double loop hypercube DLH (m,d) network is compared with mesh and torus embedded hypercube, torus embedded hypercube a better interconnection network in the properties of small diameter and highly connectivity, simple routing, scalability, constant node degree of topology, and the performance of communication. Show how to design the good interconnection network in parallel Architecture for less density as well as diameter to route the data to different path. Also it has been proved with the computational results of entire system that the embedded hypercube interconnection networks build is highly scale up in terms of communication. A complete design analysis and comparison of network with various other networks is given using different network parameters optimal of torus architecture rather than mesh architecture.
This paper is concerned with routing of data in an embedded hypercube interconnection using the approach based on neural net architecture. To present a framework of the interconnection network consist number of nodes and number of connections. In this paper we first show that n dimensional hypercube can be embedded in layer neural layer network such that for any node of hypercube, its neighboring nodes of other layer are evenly partition into layers where each layer shares a manipulating or resulting data of different layers. Under this embedding network to fixed target and varying data input to produce output of the two incidence matrix of kary n-cube network to embedded in architecture.
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