19,149 research outputs found

    ON VULNERABILITY MEASURES OF NETWORKS

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    As links and nodes of interconnection networks are exposed to failures, one of the most important features of a practical networks design is fault tolerance. Vulnerability measures of communication networks are discussed including the connectivities, fault diameters, and measures based on Hosoya-Wiener polynomial. An upper bound for the edge fault diameter of product graphs is proved

    ON VULNERABILITY MEASURES OF NETWORKS

    Get PDF
    As links and nodes of interconnection networks are exposed to failures, one of the most important features of a practical networks design is fault tolerance. Vulnerability measures of communication networks are discussed including the connectivities, fault diameters, and measures based on Hosoya-Wiener polynomial. An upper bound for the edge fault diameter of product graphs is proved

    Sparse Allreduce: Efficient Scalable Communication for Power-Law Data

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    Many large datasets exhibit power-law statistics: The web graph, social networks, text data, click through data etc. Their adjacency graphs are termed natural graphs, and are known to be difficult to partition. As a consequence most distributed algorithms on these graphs are communication intensive. Many algorithms on natural graphs involve an Allreduce: a sum or average of partitioned data which is then shared back to the cluster nodes. Examples include PageRank, spectral partitioning, and many machine learning algorithms including regression, factor (topic) models, and clustering. In this paper we describe an efficient and scalable Allreduce primitive for power-law data. We point out scaling problems with existing butterfly and round-robin networks for Sparse Allreduce, and show that a hybrid approach improves on both. Furthermore, we show that Sparse Allreduce stages should be nested instead of cascaded (as in the dense case). And that the optimum throughput Allreduce network should be a butterfly of heterogeneous degree where degree decreases with depth into the network. Finally, a simple replication scheme is introduced to deal with node failures. We present experiments showing significant improvements over existing systems such as PowerGraph and Hadoop

    New results for the degree/diameter problem

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    The results of computer searches for large graphs with given (small) degree and diameter are presented. The new graphs are Cayley graphs of semidirect products of cyclic groups and related groups. One fundamental use of our ``dense graphs'' is in the design of efficient communication network topologies.Comment: 15 page

    A multipath analysis of biswapped networks.

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    Biswapped networks of the form Bsw(G)Bsw(G) have recently been proposed as interconnection networks to be implemented as optical transpose interconnection systems. We provide a systematic construction of κ+1\kappa+1 vertex-disjoint paths joining any two distinct vertices in Bsw(G)Bsw(G), where κ1\kappa\geq 1 is the connectivity of GG. In doing so, we obtain an upper bound of max{2Δ(G)+5,Δκ(G)+Δ(G)+2}\max\{2\Delta(G)+5,\Delta_\kappa(G)+\Delta(G)+2\} on the (κ+1)(\kappa+1)-diameter of Bsw(G)Bsw(G), where Δ(G)\Delta(G) is the diameter of GG and Δκ(G)\Delta_\kappa(G) the κ\kappa-diameter. Suppose that we have a deterministic multipath source routing algorithm in an interconnection network GG that finds κ\kappa mutually vertex-disjoint paths in GG joining any 22 distinct vertices and does this in time polynomial in Δκ(G)\Delta_\kappa(G), Δ(G)\Delta(G) and κ\kappa (and independently of the number of vertices of GG). Our constructions yield an analogous deterministic multipath source routing algorithm in the interconnection network Bsw(G)Bsw(G) that finds κ+1\kappa+1 mutually vertex-disjoint paths joining any 22 distinct vertices in Bsw(G)Bsw(G) so that these paths all have length bounded as above. Moreover, our algorithm has time complexity polynomial in Δκ(G)\Delta_\kappa(G), Δ(G)\Delta(G) and κ\kappa. We also show that if GG is Hamiltonian then Bsw(G)Bsw(G) is Hamiltonian, and that if GG is a Cayley graph then Bsw(G)Bsw(G) is a Cayley graph
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