Cyclic networks: A family of versatile fixed-degree interconnection architectures

C. Yeh, B. Parhami
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引用次数: 11

Abstract

In this paper, we propose a new family of interconnection networks, called cyclic networks (CNs), in which an intercluster connection is defined on a set of nodes whose addresses are cyclic shifts of one another. The node degrees of basic CNs are independent of system size, but can vary from a small constant (e.g., 3) to as large as required, thus providing flexibility and effective tradeoff between cost and performance. The diameters of suitably constructed CNs can be asymptotically optimal within their lower bounds, given the degrees. We show that packet routing and ascend/descend algorithms can be performed in /spl Theta/(log/sub d/ N) communication steps on some CNs with N nodes of degree /spl Theta/(d). Moreover CNs can also efficiently emulate homogeneous product networks (e.g., hypercubes and high dimensional meshes). As a consequence, we obtain a variety of efficient algorithms on such networks, thus proving the versatility of CNs.
循环网络:一组通用的固定度互连体系结构
在本文中,我们提出了一种新的互连网络,称为循环网络(CNs),其中集群间连接是在一组节点上定义的,这些节点的地址是彼此的循环移位。基本神经网络的节点度与系统大小无关,但可以从一个小的常数(例如,3)变化到所需的大常数,从而在成本和性能之间提供灵活性和有效的权衡。在给定度的情况下,适当构造的神经网络的直径可以在其下界内渐近最优。我们证明了数据包路由和上升/下降算法可以在某些具有N个节点度为/spl Theta/(d)的cnn上以/spl Theta/(log/sub d/ N)通信步长执行。此外,神经网络还可以有效地模拟同质产品网络(例如,超立方体和高维网格)。因此,我们在这样的网络上获得了各种有效的算法,从而证明了神经网络的多功能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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