A compact survey of a portion of the variations of Hypercube systems, geographies, execution measurements, and assessment of the presentation are examined in this paper. Utilizing this weight metric, the performance of considered variations of Hypercube Interconnection Networks is evaluated and summed up to recognize the effective variant. A group of properties is recognized and a weight metric is structured utilizing the distinguished properties to assess the performance exhibition. This work assesses the performing capability of different variations of Hypercube Interconnection Networks. A portion of the variations of Hypercube Interconnection Networks include Hypercube Network, Folded Hypercube Network, Multiple Reduced Hypercube Network, Multiply Twisted Cube, Recursive Circulant, Exchanged Crossed Cube Network, Half Hypercube Network, and so forth. Degree, Speed, Node coverage, Connectivity, Diameter, Reliability, Packet loss, Network cost, and so on are some of the different system scales that can be used to measure the performance of Interconnection Networks. Any Hypercube can be thought of as a graph with nodes and edges, where a node represents a processing unit and an edge represents a connection between the processors to transmit. Hypercube systems are a kind of system geography used to interconnect various processors with memory modules and precisely course the information. These links transfer information from one processor to the next or from the processor to the memory, allowing the task to be isolated and measured equally. Interconnection Networks, also known as Multi-stage Interconnection Networks, are node-to-node links in which each node may be a single processor or a group of processors. An Interconnection Network interfaces the various handling units and enormously impacts the exhibition of the whole framework. The principle goal of utilizing a multiprocessor is to process the undertakings all the while and support the system’s performance. If the RD of each node is greater than 4, then the SLD of each node is also equal to its RD, no matter how many faulty edges exist in Qn.Ī Multiprocessor is a system with at least two processing units sharing access to memory. Finally, we discuss the SLD of a node for an incomplete hypercube network and obtain the following results: if the minimum RD of nodes in an incomplete hypercube network of n-dimensions is greater than 3, then the SLD of any node is still equal to its RD provided that the number of faulty edges does not exceed 7n−3−1. We determine that the SLD of a node is equal to its remaining degree (RD) in an incomplete hypercube network, which is true provided that the number of faulty edges in this hypercube network does not exceed n−3. Moreover, we explore the SLD of a node of an incomplete hypercube network. Based on these results, we conclude that in a hypercube network of n dimensions, denoted by Qn, the SLD of a node is equal to its degree when n⩾4. In addition, a few important results related to the SLD of a node of a system are presented. In this article, a new concept regarding diagnosability, called strong local diagnosability (SLD), which describes the local status of the strong diagnosability (SD) of a system, is presented. and so on.In the research on the reliability of a connection network, diagnosability is an important problem that should be considered. A 4d hypercube has 8 faces (which are cubes). So a line segment has 2 faces (which are points). One with $v_i = -1$ and the other with $v_i = 1$. How many $(n-1)$d faces does it have? How do you derive that?įor the faces I think we can say: pick a dimension $i$, partition the verticies into two sets of $2^$ verticies. How many edges does it have as a function of n? How do you derive that? , v_n)$ then choose some subset and set them to $1$, set the rest to $-1$. (If we imagine a vertex coordinate $v = (v_1, v_2. So the faces of a cube are squares.Īn nd hypercube has $2^n$ verticies. We can define the faces of an nd hypercube as being (n-1)d hypercubes. It has 8 verticies and 12 edges and 6 faces.Ī 4d hypercube has 16 verticies and not sure how many edges or 3d faces. Again not sure how many faces it has?Ī 3d hypercube is a cube. Not sure how many faces it has?Ī 2d hypercube is a square. So I guess a 1d hypercube is a line segment.
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