In large-scale data centers, many servers are in- terconnected via a dedicated networking structure, so as to satisfy specific design goals, such as the low equipment cost, the high network capacity, and the increment...In large-scale data centers, many servers are in- terconnected via a dedicated networking structure, so as to satisfy specific design goals, such as the low equipment cost, the high network capacity, and the incremental expansion. The topological properties of a networking structure are criti- cal factors that dominate the performance of the entire data center. The existing networking structures are either fully random or completely structured. Although such networking structures exhibit advantages on given aspects, they suffer ob- vious shortcomings in other essential fields. In this paper, we aim to design a hybrid topology, called R3, which is the com- pound graph of structured and random topology. It employs random regular graph as a unit duster and connects many such clusters by means of a structured topology, i.e., the gen- eralized hypercube. Consequently, the hybrid topology com- bines the advantages of structured as well as random topolo- gies seamlessly. Meanwhile, a coloring-based algorithm is proposed for R3 to enable fast and accurate routing. R3 pos- sesses many attractive characteristics, such as the modularity and expansibility at the cost of only increasing the degree of any node by one. Comprehensive evaluation results show that our hybrid topology possesses excellent topology properties and network performance.展开更多
Building façades can feature different patterns depending on the architectural style,function-ality,and size of the buildings;therefore,reconstructing these façades can be complicated.In particular,when sema...Building façades can feature different patterns depending on the architectural style,function-ality,and size of the buildings;therefore,reconstructing these façades can be complicated.In particular,when semantic façades are reconstructed from point cloud data,uneven point density and noise make it difficult to accurately determine the façade structure.When inves-tigating façade layouts,Gestalt principles can be applied to cluster visually similar floors and façade elements,allowing for a more intuitive interpretation of façade structures.We propose a novel model for describing façade structures,namely the layout graph model,which involves a compound graph with two structure levels.In the proposed model,similar façade elements such as windows are first grouped into clusters.A down-layout graph is then formed using this cluster as a node and by combining intra-and inter-cluster spacings as the edges.Second,a top-layout graph is formed by clustering similar floors.By extracting relevant parameters from this model,we transform semantic façade reconstruction to an optimization strategy using simulated annealing coupled with Gibbs sampling.Multiple façade point cloud data with different features were selected from three datasets to verify the effectiveness of this method.The experimental results show that the proposed method achieves an average accuracy of 86.35%.Owing to its flexibility,the proposed layout graph model can deal with different types of façades and qualities of point cloud data,enabling a more robust and accurate reconstruc-tion of façade models.展开更多
This paper introduces a novel interconnection network called KMcube （Kautz-Mobius cube）. KMcube is a compound graph of a Kautz digraph and M/Sbius cubes. That is, it uses the Mobius cubes as the unit cluster and con...This paper introduces a novel interconnection network called KMcube （Kautz-Mobius cube）. KMcube is a compound graph of a Kautz digraph and M/Sbius cubes. That is, it uses the Mobius cubes as the unit cluster and connects many such clusters by means of a Kautz digraph at the cost of only one additional arc being added to any node in each Mobius cubes. The topological benefits of both basic graphs are preserved in the compound network. It utilizes the topo- logical properties of Mobius cubes to conveniently embed parallel algorithms into each cluster and the short diameter of a Kautz digraph to support efficient inter-cluster communi- cation. Additionally, KMcube provides other attractive prop- erties, such as the regularity, symmetry, and expandability. The proposed methodology for KMcube is further applied to the compound graphs of Kautz digraph and other Mobius-like graphs with the similar diameter to a Mobius cube. Moreover, other hybrid graphs of Kautz digraph and Mobius cubes are proposed and compared.展开更多
文摘In large-scale data centers, many servers are in- terconnected via a dedicated networking structure, so as to satisfy specific design goals, such as the low equipment cost, the high network capacity, and the incremental expansion. The topological properties of a networking structure are criti- cal factors that dominate the performance of the entire data center. The existing networking structures are either fully random or completely structured. Although such networking structures exhibit advantages on given aspects, they suffer ob- vious shortcomings in other essential fields. In this paper, we aim to design a hybrid topology, called R3, which is the com- pound graph of structured and random topology. It employs random regular graph as a unit duster and connects many such clusters by means of a structured topology, i.e., the gen- eralized hypercube. Consequently, the hybrid topology com- bines the advantages of structured as well as random topolo- gies seamlessly. Meanwhile, a coloring-based algorithm is proposed for R3 to enable fast and accurate routing. R3 pos- sesses many attractive characteristics, such as the modularity and expansibility at the cost of only increasing the degree of any node by one. Comprehensive evaluation results show that our hybrid topology possesses excellent topology properties and network performance.
基金This work is supported by the National Natural Science Foundation of China[grant number 41771484].
文摘Building façades can feature different patterns depending on the architectural style,function-ality,and size of the buildings;therefore,reconstructing these façades can be complicated.In particular,when semantic façades are reconstructed from point cloud data,uneven point density and noise make it difficult to accurately determine the façade structure.When inves-tigating façade layouts,Gestalt principles can be applied to cluster visually similar floors and façade elements,allowing for a more intuitive interpretation of façade structures.We propose a novel model for describing façade structures,namely the layout graph model,which involves a compound graph with two structure levels.In the proposed model,similar façade elements such as windows are first grouped into clusters.A down-layout graph is then formed using this cluster as a node and by combining intra-and inter-cluster spacings as the edges.Second,a top-layout graph is formed by clustering similar floors.By extracting relevant parameters from this model,we transform semantic façade reconstruction to an optimization strategy using simulated annealing coupled with Gibbs sampling.Multiple façade point cloud data with different features were selected from three datasets to verify the effectiveness of this method.The experimental results show that the proposed method achieves an average accuracy of 86.35%.Owing to its flexibility,the proposed layout graph model can deal with different types of façades and qualities of point cloud data,enabling a more robust and accurate reconstruc-tion of façade models.
基金This work was partially supported by the National Natural Science Foundation of China （Grant Nos. 61170284, 60903206, 61070216, and 71071160）, the China Postdoctoral Science Foundation （20100480898 and 201104439）, the Science Study Foundation of Doctorial Subject for High Schools （20114307110011）, the Hunan Provincial Innova- tion Foundation for Postgraduate （CX2010B022）, and the NUDT Innovation Foundation for Postgraduate （B100501）.
文摘This paper introduces a novel interconnection network called KMcube （Kautz-Mobius cube）. KMcube is a compound graph of a Kautz digraph and M/Sbius cubes. That is, it uses the Mobius cubes as the unit cluster and connects many such clusters by means of a Kautz digraph at the cost of only one additional arc being added to any node in each Mobius cubes. The topological benefits of both basic graphs are preserved in the compound network. It utilizes the topo- logical properties of Mobius cubes to conveniently embed parallel algorithms into each cluster and the short diameter of a Kautz digraph to support efficient inter-cluster communi- cation. Additionally, KMcube provides other attractive prop- erties, such as the regularity, symmetry, and expandability. The proposed methodology for KMcube is further applied to the compound graphs of Kautz digraph and other Mobius-like graphs with the similar diameter to a Mobius cube. Moreover, other hybrid graphs of Kautz digraph and Mobius cubes are proposed and compared.
