Star-crossed cube: an alternative to star graph

Adhikari, Nibedita; Tripathy, C. R.

doi:10.3906/elk-1202-44

Cited by 13 publications

(9 citation statements)

References 20 publications

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“…The paths where the other edges except edge e are mapped are edge-disjointed with each other, so congestion is 1. However, on the edge (3,6) of H, the path where the edges (3,6) and (3,7) of G are mapped goes, congestion is 2. Therefore, in the embedding of Figure 1 [21].…”

Section: Related Work and Preliminariesmentioning

confidence: 99%

“…Star graph class is mostly based on how to generate a node in star graph and has difference in how to generate an edge. Star graph class includes hyperstar HS(m,k) [6], star-crossed cube SCQ(m,n) [7], folded hyper-star FHS(2n,n) [8], hierarchical star HS(n,n) [9], macro star MS(m,n) [10], transposition graph [11], matrix star [12], pancake graph [13], and half pancake graph [15].…”

Section: Introductionmentioning

confidence: 99%

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Embedding Algorithm among Half Pancake, Pancake, and Star Graphs

Seo

Lee

2016

IJSEIA

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The star graph is an attractive alternative to hypercube. The pancake graph has n! nodes and generates edge using exchange of a symbol that composes a node address like star graph. The half pancake graph is a new network where the network cost of pancake graph is reduced by half. We suggest embedding algorithm among these networks. The half pancake HP n was embedded on pancake P n with dilation 1 and congestion 1. The half pancake P n was embedded on star graph S n with dilation 1.5n-2, average dilation about 0.25n+4, and congestion 6. The pancake P n was embedded on star graph S n with dilation 1.5n. All the three of embedding were one-to-one embedding with expansion 1.

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Section: Related Work and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Embedding Algorithm among Half Pancake, Pancake, and Star Graphs

Seo

Lee

2016

IJSEIA

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“…6a, the first submodule with the star part labeled as 123 and the corresponding labels of the CC are shown. Similarly, Figure 6b shows the second submodule of the SCC (3,3) with 213 as their star part level [18]. The total number of nodes and degree in the SMC (k,m) graph are k!2 m and m+k-1 respectively.…”

Section: Star Crossed Cube (Scc)mentioning

confidence: 99%

A Comparative Study of Interconnection Network

Alam¹,

Varshney²

2015

IJCA

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The topology of interconnection networks the stage a key role in the performance of all general purpose networking applications. Cube-based architectures are one of the most important interconnection networks that focuses upon the evaluation and applications of cube-based networks. Cubebased architectures have received greatly focus over the past decade since they propose a wealthy interconnected structure with a number of attractive properties such as low diameter, high bisection width, smaller complexity and Cost. However, the major drawback of cube-based architectures is the difficulty of its VLSI layout. In Parallel computer, the hypercube network has been broadly used as the interconnection network. However, the number of communication links for each node is a logarithmic function of the total number of nodes in hypercubes. Therefore, the hypercube is not a superior applicant for an interconnection network for a extremely large parallel computer that might contain hundreds of thousands of nodes due to IC technology and port number restrictions. In this paper a variety of interconnection network based on the cube-based networks is brief discussed along with their properties. X-torus topology has better properties in terms of diameter, average latency, throughput, and path diversity. Although some more links are added in xtorus, the number of links is of the same order of magnitude with that of mesh, xmesh, and torus. It also takes advantage of increasing higher levels of VLSI process. The comparative study suggests the methods to overcome the above restrictions besides having attractive properties.

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“…The design of efficient routing algorithms has received significant attention due to its importance to high performance in multicomputer systems [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Many applications that run on multicomputer systems depend highly on communication time.…”

Section: Introductionmentioning

confidence: 99%

Routing Capabilities of Mesh-Hypercube Network

Omari¹

2019

IJSAIT

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The aim of this research is to develop optimal packet routing strategies for the Mesh Hypercube (MH) network of arbitrary size. MH has been introduced as a new interconnection network for multicomputer systems. The basic structure for this network is a combination of both mesh and hypercube networks. In this research, the parallel path property in the MH is studied and the length and the number of parallel shortest paths are computed. An optimal packet routing algorithm is given which uses minimal length path. The algorithm is simple and appears to be well suited to VLSI implementation. The simulation results of the proposed algorithm on various network designs showed that it performs very well at various network conditions. In a MH of size mn nodes, the algorithm performs outstandingly when nm. Specifically, the best results of both waiting-time and packet-latency network performance measures were achieved when m=4 and n=16, while the worst values of all measures were obtained when m=8 and n=8.

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Star-crossed cube: an alternative to star graph

Cited by 13 publications

References 20 publications

Embedding Algorithm among Half Pancake, Pancake, and Star Graphs

Embedding Algorithm among Half Pancake, Pancake, and Star Graphs

A Comparative Study of Interconnection Network

Routing Capabilities of Mesh-Hypercube Network

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