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Hypercube internetwork topology. In computer networking, hypercube networks are a type of network topology used to connect and route data between multiple processing units or computers. Hypercube networks consist of 2m nodes, which form the vertices of squares to create an internetwork connection. A hypercube is basically a multidimensional ...
In geometry, a hypercube is an n -dimensional analogue of a square ( n = 2) and a cube ( n = 3 ). It is a closed, compact, convex figure whose 1- skeleton consists of groups of opposite parallel line segments aligned in each of the space's dimensions, perpendicular to each other and of the same length. A unit hypercube's longest diagonal in n ...
The iPSC used a Hypercube internetwork topology of connections between the processors internally inspired by the Caltech Cosmic Cube research project. For that reason, it was configured with nodes numbering with power of two, which correspond to the corners of hypercubes of increasing dimension.
Hypercube graph. In graph theory, the hypercube graph Qn is the graph formed from the vertices and edges of an n -dimensional hypercube. For instance, the cube graph Q3 is the graph formed by the 8 vertices and 12 edges of a three-dimensional cube. Qn has 2n vertices, 2n – 1n edges, and is a regular graph with n edges touching each vertex.
Internetworking. Internetworking is the practice of interconnecting multiple computer networks, [1] : 169 such that any pair of hosts in the connected networks can exchange messages irrespective of their hardware-level networking technology. The resulting system of interconnected networks are called an internetwork, or simply an internet .
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Cube-connected cycles. The cube-connected cycles of order 3, arranged geometrically on the vertices of a truncated cube. In graph theory, the cube-connected cycles is an undirected cubic graph, formed by replacing each vertex of a hypercube graph by a cycle. It was introduced by Preparata & Vuillemin (1981) for use as a network topology in ...
Angela Y. Wu. Angela Yuen Wu is an American computer scientist, a professor emerita at American University. [1] She is known for her research in computer vision and computational geometry, and especially for her highly cited publications on k-means clustering [KM] and nearest neighbor search. [NN] Other topics in her research include embeddings ...
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