Embedding algorithm of spined cube into grid structure and its wirelength computation

In the field of data science, graph embedding is a vital tool to simulate, visualize and design a parallel architecture. The binary cube is one of the most desirable and reliable network-based architecture with high performance, yet many variations have been proposed on the links of the cube to enhance its computational power. The spined cube is a new efficient variant of the binary cube with a smaller diameter and preserves the advantageous properties of the original cube. Grid embedding is accustomed to study simulation capacity of a parallel and distributed computer system and conjointly designs its VLSI layout. In this paper, we propose an algorithm to embed the spined cube into a grid and compute the minimum wirelength over all possible embeddings using the edge congestion technique. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

In this paper, the importance of graph embedding in the field of data science is discussed, along with the characteristics and improvements of network-based architectures like binary cube and spined cube. Furthermore, an algorithm for embedding the spined cube into a grid and computing the minimum wirelength is proposed using the edge congestion technique.