Component diagnosability in terms of component connectivity of hypercube-based compound networks

Liu, Jiafei; Zhou, Shuming; Wang, Dajin; Zhang, Hong

doi:10.1016/j.jpdc.2021.12.004

Cited by 16 publications

(6 citation statements)

References 39 publications

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“…3. The (h + 1)-component connectivity and (h + 1)-component diagnosability of half hypercube was researched by Liu et al 31 4. The g-good neighbor conditional diagnosabilities and R g -connectivity of half hypercube was studied by Lv et al, in Reference 32.…”

Section: Studies Specific To the Half Hypercubementioning

confidence: 99%

“…A novel interconnection network called half hypercube uses hypercubes as its fundamental building components. Due to its reduced vertex degree, smaller diameter, average distance, and elegant routing technique, the half hypercube is evidently superior to hypercube 30–32 . Reduced vertex degree improves the scalability of network by lowering the quantity of hardware utilized.…”

Section: Introductionmentioning

confidence: 99%

“…Here are the prominent results that appeared on various aspects of the half hypercube during the recent times. The reliability measures on half hypercube were researched in Reference 36. In Reference 30, Kim et al, established the connectivity, routing and diameter properties of half hypercube

{\mathrm{HQ}}_n

. The

\left(h+1\right)

‐component connectivity and

\left(h+1\right)

‐component diagnosability of half hypercube was researched by Liu et al 31 The

g

‐good neighbor conditional diagnosabilities and

{R}^g

‐connectivity of half hypercube was studied by Lv et al, in Reference 32. …”

Section: Introductionmentioning

confidence: 99%

“…Due to its reduced vertex degree, smaller diameter, average distance, and elegant routing technique, the half hypercube is evidently superior to hypercube. [30][31][32] Reduced vertex degree improves the scalability of network by lowering the quantity of hardware utilized. Half hypercube possess minimum diameter and average distance when compared to a hypercube having same number of vertices.…”

mentioning

confidence: 99%

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Optimization of layout for embedding half hypercube into conventional tree architectures

Immanuel,

Greeni

2024

Concurrency and Computation

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SummaryEmbedding a graph into another graph can be utilized for structural simulation, processor allocation, and algorithm porting in the field of parallel architecture. This has the potential to enhance the physical layout of network‐on‐chip (NoC) devices as well as to investigate their virtualization possibilities. Layout is one of the many indicators of graph embedding. An optimal layout in NoC design can result in a decreased wiring area and cost, as well as the reduction in communication delay between parallel processing components. In this work, the guest graph is the half hypercube, which possess efficient routing, fault tolerance, and Hamiltonian features. The edge isoperimetric problems is solved for the half hypercube using a novel technique called the isochronal ordering. The host graphs considered in our work are complete binary trees, ‐rooted complete binary trees, and other few predominantly investigated tree based architectures.

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Section: Studies Specific To the Half Hypercubementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

{\mathrm{HQ}}_n

. The

\left(h+1\right)

‐component connectivity and

\left(h+1\right)

‐component diagnosability of half hypercube was researched by Liu et al 31 The

g

‐good neighbor conditional diagnosabilities and

{R}^g

‐connectivity of half hypercube was studied by Lv et al, in Reference 32. …”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Optimization of layout for embedding half hypercube into conventional tree architectures

Immanuel,

Greeni

2024

Concurrency and Computation

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“…In the MM* model, if any two nodes share a common neighbor, that common neighbor must act as the arbitrator for the two nodes, undergoing testing and judgment. Numerous works on the MM* model have been developed [6,13,14,16,21,29].…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of complete cubic networks based on extra conditional fault

Lv,

Liu,

Dong

et al. 2024

Preprint

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The reliability of multiprocessor systems is now a crucial concern in parallel computing. In fact, thanks to rapid and consistent technological advances in networking hardware and software, multiprocessor (core) systems have been successfully implemented. Consequently, there is a notable importance in examining the structures of interprocess communication, with a particular focus on investigating the fault tolerance and fault diagnosability of these structures to build a functionally efficient system. This paper investigates the $s$-extra connectivity ($s$-EC) and $s$-extra diagnosability ($s$-ED) of the complete cubic network $CCN(n)$. Specifically, we initially demonstrate that the $s$-EC of $CCN(n)$ is $\kappa_s(CCN(n))=(s+1)(n+1)-\frac{s(s+3)}{2}$ for $n\geq 3$ and $0\leq s\leq n-2$. Subsequently, we demonstrate that the $s$-ED under the PMC model is $t_s(CCN(n))=(s+1)(n+1)-\frac{s(s+3)}{2}+s$ for $n\geq 3$ and $1\leq s\leq n-2$. Similarly, under the MM* model, the $s$-ED is $t_s(CCN(n))=(s+1)(n+1)-\frac{s(s+3)}{2}+s$ for $n\geq 6$ and $1\leq s\leq \frac{n-2}{4}$. Finally, we conduct simulation experiments, and the results indicate that the $s$-EC consistently surpasses other known connectivities, including classical connectivity and $s$-component connectivity. Additionally, the $s$-ED consistently outperforms classical diagnosability and $s$-component diagnosability of $CCN(n)$.

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Hamiltonian cycle embedding with fault-tolerant edges and adaptive diagnosis in half hypercube

Fan,

Liu,

2023

J Supercomput

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Interconnection network directly affects the efficiency of parallel and distributed systems. Hypercube is one of the most important interconnection networks in parallel computing systems. As a significant variant of hypercube, half hypercube has the same number of vertices as hypercube while its degree is only half that of hypercube, which indicates that it has lower network overhead than hypercube. In this paper, we study the fault-tolerant embedding of Hamiltonian cycles and design an adaptive diagnosis algorithm for the half hypercube. Firstly, we prove that the half hypercube is Hamiltonian and propose an algorithm to construct a Hamiltonian cycle in the network. Furthermore, we prove that half hypercube is Hamiltonian with no more than (⌈n/2⌉−1) faulty edges. Finally, we present a parallel adaptive diagnosis scheme under PMC model, which can identify almost all faulty vertices in five rounds.

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Component diagnosability in terms of component connectivity of hypercube-based compound networks

Cited by 16 publications

References 39 publications

Optimization of layout for embedding half hypercube into conventional tree architectures

Optimization of layout for embedding half hypercube into conventional tree architectures

Reliability analysis of complete cubic networks based on extra conditional fault

Hamiltonian cycle embedding with fault-tolerant edges and adaptive diagnosis in half hypercube

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