Symmetric Properties and Two Variants of Shuffle-Cubes

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Huazhong Lü;Kai Deng;Xiaomei Yang
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引用次数: 0

Abstract

Li et al. in [Inf. Process. Lett. 77 (2001) 35–41] proposed the shuffle-cube $SQ_{n}$, a hypercube variant, as an attractive interconnection network topology for massive parallel and distributed systems. Diameter and symmetry are two desirable measures of network performance in terms of transmission delay and routing algorithms. Almost all $n$-regular hypercube variants of dimension $n$ have diameter not less than $n/2$. The diameter of the shuffle-cube is approximately a quarter of the diameter of the hypercube of the same dimension, making it a competitive candidate network topology. By far, symmetric properties of the shuffle-cube remain unknown. In this paper, we show that $SQ_{n}$ is not vertex-transitive for $n> 2$, which is not an appealing property in interconnection networks. This shortcoming limits the practical application of the shuffle-cube. To overcome this limitation, two novel variants of the shuffle-cube, namely simplified shuffle-cube $SSQ_{n}$ and balanced shuffle-cube $BSQ_{n}$ are introduced, and their vertex-transitivity are proved simultaneously. By proposing the shuffle-cube-like graph, we obtain that both $SSQ_{n}$ and $BSQ_{n}$ are maximally connected, implying high connectivity similar to the hypercube. Additionally, super-connectivity, a refined parameter of connectivity, of $SSQ_{n}$ and $BSQ_{n}$ are also determined. Then, by vertex-transitivity of $SSQ_{n}$ and $BSQ_{n}$, routing algorithms of $SSQ_{n}$ and $BSQ_{n}$ are given for all $n> 2$ respectively. We show that both $SSQ_{n}$ and $BSQ_{n}$ possess Hamiltonian cycle embedding for all $n> 2$, and we also show that $SSQ_{n}$ is Hamiltonian-connected. It is noticeable that each vertex of $SSQ_{n}$ is contained in exactly one clique of size four, making it also a viable interconnection topology for data center networking since each clique of size four can be viewed as an efficient local data processing cluster of the network. Finally, as a by-product of proving vertex-transitivity of $BSQ_{n}$, we mend a flaw in the Property 3 in [IEEE Trans. Comput. 46 (1997) 484–490].
洗牌立方体的对称性质和两种变体
Li et al. in [Inf. Process]。Lett. 77(2001) 35-41]提出了shuffle-cube $SQ_{n}$,一种超立方体变体,作为大规模并行和分布式系统的有吸引力的互连网络拓扑。在传输延迟和路由算法方面,直径和对称性是两个理想的网络性能度量。几乎所有维度为$n$的$n$-正则超立方体变体的直径都不小于$n/2$。洗牌立方体的直径大约是相同维度的超立方体直径的四分之一,这使得它成为竞争的候选网络拓扑。到目前为止,洗牌方块的对称性质仍然未知。在本文中,我们证明了$SQ_{n}$对于$n> $不是顶点传递的;2美元,这在互连网络中不是一个吸引人的属性。这个缺点限制了洗牌方块的实际应用。为了克服这一限制,引入了简化的shuffle-cube $SSQ_{n}$和平衡的shuffle-cube $BSQ_{n}$两种新的shuffle-cube变体,并同时证明了它们的顶点可传递性。通过提出shuffle-cube-类图,我们得到$SSQ_{n}$和$BSQ_{n}$都是最大连接的,这意味着与超立方体类似的高连通性。此外,还确定了$SSQ_{n}$和$BSQ_{n}$的超连通性,即连通性的细化参数。然后,根据$SSQ_{n}$和$BSQ_{n}$的顶点传递性,给出了$SSQ_{n}$和$BSQ_{n}$对所有$n>的路由算法;分别为2美元。我们证明了$SSQ_{n}$和$BSQ_{n}$对所有$n>都具有哈密顿循环嵌入;我们还证明了$SSQ_{n}$是哈密顿连通的。值得注意的是,$SSQ_{n}$的每个顶点恰好包含在一个大小为4的团中,这使得它也是数据中心网络的可行互连拓扑,因为每个大小为4的团都可以被视为网络的一个有效的本地数据处理集群。最后,作为证明$BSQ_{n}$顶点可传递性的副产品,我们修正了[IEEE Trans]中性质3的一个缺陷。计算机学报,46 (1997)484-490 [j]。
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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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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