Folded Petersen cube networks: new competitors for the hypercubes

Öhring, Sabine R.; Das, Sajal K.

doi:10.1109/spdp.1993.395482

Cited by 23 publications

(28 citation statements)

References 24 publications

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“…These include the Binary Hypercube (BHC) [8], the Generalized Hypercube (GHC) [12], the Torus [34], the Spanning Bus Hypercube (SBH) [21], the Hierarchical Cubic Network (HCN) [9], the Cube-Connected-Cycle (CCC) [35], the Hyper-deBruijn (HdB) [36], the Folded Peterson (FPT) [37], the Hypermesh (HM) [33], and the Optical Multi-Mesh Hypercube (OMMH) [17]. The comparison parameters include diameter, degree, number of links, average message distance, average traffic density, and queuing delay.…”

Section: Comparisons Of Smlh With Popular Networkmentioning

confidence: 99%

A spanning multichannel linked hypercube: a gradually scalable optical interconnection network for massively parallel computing

Louri

Weech

Neocleous

1998

IEEE Trans. Parallel Distrib. Syst.

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Abstract-A new, scalable interconnection topology called the Spanning Multichannel Linked Hypercube (SMLH) is proposed. This proposed network is very suitable to massively parallel systems and is highly amenable to optical implementation. The SMLH uses the hypercube topology as a basic building block and connects such building blocks using two-dimensional multichannel links (similar to spanning buses). In doing so, the SMLH combines positive features of both the hypercube (small diameter, high connectivity, symmetry, simple routing, and fault tolerance) and the spanning bus hypercube (SBH) (constant node degree, scalability, and ease of physical implementation), while at the same time circumventing their disadvantages. The SMLH topology supports many communication patterns found in different classes of computation, such as bus-based, mesh-based, and tree-based problems, as well as hypercube-based problems. A very attractive feature of the SMLH network is its ability to support a large number of processors with the possibility of maintaining a constant degree and a constant diameter. Other positive features include symmetry, incremental scalability, and fault tolerance. It is shown that the SMLH network provides better average message distance, average traffic density, and queuing delay than many similar networks, including the binary hypercube, the SBH, etc. Additionally, the SMLH has comparable performance to other high-performance hypercubic networks, including the Generalized Hypercube and the Hypermesh. An optical implementation methodology is proposed for SMLH. The implementation methodology combines both the advantages of free space optics with those of wavelength division multiplexing techniques. A detailed analysis of the feasibility of the proposed network is also presented.

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Section: Comparisons Of Smlh With Popular Networkmentioning

confidence: 99%

A spanning multichannel linked hypercube: a gradually scalable optical interconnection network for massively parallel computing

Louri

Weech

Neocleous

1998

IEEE Trans. Parallel Distrib. Syst.

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“…The bidirectional ring topology is more scalable, but it is not fault-tolerant. Hypercube [2] and its variants FPCN [3], de Bruijn [4] and its variants , Kautz [5] and ShuffleNet [6] have a number of node restrictions. They are either not scalable or not faulttolerant.…”

Section: Introductionmentioning

confidence: 99%

Binomial Graph: A Scalable and Fault-Tolerant Logical Network Topology

Angskun

Bosilca

Dongarra

2007

Parallel and Distributed Processing and Applications

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Abstract. The number of processors embedded in high performance computing platforms is growing daily to solve larger and more complex problems. The logical network topologies must also support the high degree of scalability in dynamic environments. This paper presents a scalable and fault tolerant topology called binomial graph (BMG). BMG provides desirable topological properties in terms of both scalability and fault-tolerance for high performance computing such as reasonable degree, regular graph, low diameter, symmetric graph, low cost factor, low message traffic density, optimal connectivity, low fault-diameter and strongly resilient. Several fault-tolerant routing algorithms are provided on BMG for various message types. More importantly, BMG is able to deliver broadcast messages from any node within log2(n) steps.

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“…The bidirectional ring topology is more scalable, but it is not fault-tolerant. Hypercube [2] and its variants [3][4][5][6][7][8][9][10], FPCN [11], de Bruijn [12] and its variants [13,14], Kautz [15] and ShuffleNet [16] have a number of node restrictions. They are either not scalable or not fault-tolerant.…”

Section: Introductionmentioning

confidence: 99%

Optimal Routing in Binomial Graph Networks

Liu

2007

Eighth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2007)

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A circulant graph with n nodes and jumps j 1 , j 2 , ..., j m is a graph in which each node i, 0 ≤ i ≤ n − 1, is adjacent to all the vertices i ± j k mod n, where 1 ≤ k ≤ m. A binomial graph network (BMG) is a circulant graph where j k is the power of 2 that is less than or equal to n. This paper presents an optimal (shortest path) two-terminal routing algorithm for BMG networks. This algorithm uses only the destination address to determine the next hop in order to stay on the shortest path. Unlike the original algorithms, it does not require extra space for routing tables or additional information in the packet. The experimental results show that the new optimal algorithm is significantly faster than the original optimal algorithm.

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Folded Petersen cube networks: new competitors for the hypercubes

Cited by 23 publications

References 24 publications

A spanning multichannel linked hypercube: a gradually scalable optical interconnection network for massively parallel computing

A spanning multichannel linked hypercube: a gradually scalable optical interconnection network for massively parallel computing

Binomial Graph: A Scalable and Fault-Tolerant Logical Network Topology

Optimal Routing in Binomial Graph Networks

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