首页> 外文期刊>Journal of interconnection networks >APPROXIMATING HYPERCUBES BY INDEX-SHUFFLE GRAPHS VIA DIRECT-PRODUCT EMULATIONS
【24h】

APPROXIMATING HYPERCUBES BY INDEX-SHUFFLE GRAPHS VIA DIRECT-PRODUCT EMULATIONS

机译:通过直接乘积仿真通过指数-舒勒图逼近超高温

获取原文
获取原文并翻译 | 示例
       

摘要

Index-shuffle graphs are a family of bounded-degree hypercube-like interconnection networks for parallel computers, introduced by [Baumslag and Obrenic (1997): Index-Shuffle Graphs, ...], as an efficient substitute for two standard families of hypercube derivatives: butterflies and shuffle-exchange graphs. In the theoretical framework of graph embedding and network emulations, this paper shows that the index-shuffle graph efficiently approximates the direct-product structure of the hypercube, and thereby has a unique potential to approximate efficiently all of its derivatives. One of the consequences of our results is that any member of the following group of standard bounded-degree hypercube derivatives: butterflies, shuffles, tori, meshes of trees, is emulated by the index-shuffle graph with a slowdown in the order of the logarithm of the slowdown of the most efficient emulation achieved by any other member of this group. Emulation algorithms are presented where the emulation host is the n-dimensional index-shuffle graph Ψ_n, having N = 2~n nodes. The emulated graph G is a direct product of the form: G = F_0 x F_1 x ··· x F_(k-1) where k is a power of 2, and each factor F_i is an instance of any of the following three graph families: cycle, complete binary tree, X-tree. Let the size of each factor be |F_i| ≤ 2~(n_f), where k · n_f ≤ n. The index-shuffle graph Ψ_n emulates any factor F_i in the product G with slowdown: O(log k) + O(log n_f), which is O(log n) = O(log log N). Any collection of 2~l copies of the product G, such that: l + k · n_f ≤ n is emulated by the index-shuffle graph Ψ_n simultaneously, without any additional slowdown. Relaxing the assumption that k is a power of 2 introduces an additional factor of O(lo~* N) into the slowdown.
机译:索引混洗图是并行计算机的有界度超立方体状互连网络家族,由[Baumslag and Obrenic(1997):Index-Shuffle Graphs,...]引入,作为两个标准超立方体族的有效替代品导数:蝴蝶图和随机交换图。在图嵌入和网络仿真的理论框架中,本文表明索引改组图有效地逼近了超立方体的直接乘积结构,从而具有独特的潜力来高效地逼近其所有派生。结果的后果之一是,以下一组标准有界超超立方体导数的任何成员:蝴蝶,随机,圆托,树状网格,均由index-shuffle图模拟,其对数减慢该组中任何其他成员所实现的最有效仿真的速度下降。提出了仿真算法,其中仿真主机是具有N = 2〜n个节点的n维索引-随机图graph_n。仿真图G是以下形式的直接积:G = F_0 x F_1 x··x F_(k-1)其中k是2的幂,并且每个因子F_i是以下三个图中任何一个的实例家族:循环,完整的二叉树,X树。令每个因子的大小为| F_i | ≤2〜(n_f),其中k·n_f≤n。索引洗牌图Ψ_n模拟了乘积G中的任何因子F_i,且速度减慢:O(log k)+ O(log n_f),即O(log n)= O(log log N)。产品G的2〜l个副本的任何集合,例如:l + k·n_f≤n,同时由索引改组图Ψ_n模拟,而没有任何其他减慢。放宽k为2的幂的假设,会在减速中引入O(lo〜* N)的附加因子。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号