2006
DOI: 10.1007/s10766-006-0014-1
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Dense Gaussian Networks: Suitable Topologies for On-Chip Multiprocessors

Abstract: This paper explores the suitability of dense circulant graphs of degree four for the design of on-chip interconnection networks. Networks based on these graphs reduce the Torus diameter in a factor √ 2 which translates into significant performance gains for unicast traffic. In addition, they are clearly superior to Tori when managing collective communications. This paper introduces a new two-dimensional node's labeling of the networks explored which simplifies their analysis and exploitation. In particular, it… Show more

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Cited by 28 publications
(19 citation statements)
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“…In [1], [15], [16], [17], [18] and [19], it has been shown that hexagonal torus networks are a special family of EJ networks where the nodes of the graph are represented by EJ integers. A node <x, y> corresponds to a complex number x+yw where x, y are integers and w a complex number w= .…”
Section: Fig 1 Hexagonal Torus H4mentioning
confidence: 99%
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“…In [1], [15], [16], [17], [18] and [19], it has been shown that hexagonal torus networks are a special family of EJ networks where the nodes of the graph are represented by EJ integers. A node <x, y> corresponds to a complex number x+yw where x, y are integers and w a complex number w= .…”
Section: Fig 1 Hexagonal Torus H4mentioning
confidence: 99%
“…Dense EJ networks are thus good candidate for NoC interconnection networks. It has been shown in [15], [16] the isomorphism between the dense EJ networks, hexagonal torus and Circulant graphs.…”
Section: Fig 1 Hexagonal Torus H4mentioning
confidence: 99%
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“…Applicable alternative regular topologies that have been shown to be suitable for on-chip networks include the mesh, ring and circulant graph topologies [18].…”
Section: B Distributed Test Vector Storagementioning
confidence: 99%
“…For example, there is a Gaussian network with 200 nodes with diameter 10, whereas, any 2D toroidal network with 200 nodes will have a diameter of at least 15. There are many similarities between the Gaussian and EJ cases. We have tried to emphasize these connections when possible since the EJ networks are not well-understood compared to the significant work on Gaussian interconnection networks (for example, [17], [19], [20]). One reason for the scarcity of EJ results is that distance in EJ networks is more difficult to compute from the definition since the degree of each vertex is 6.…”
Section: Introductionmentioning
confidence: 99%