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一种高性能、低成本的高密度服务器互连网络

A High-Performance and Cost-Effcient Interconnection Network for High-Density Servers

  • 摘要: 高密度服务器具有功耗低、体积小以及计算密度高等优点。随着数据密集型和大规模网络应用对高密度服务器的广泛采用,为其设计具有高性能和低成本优势的内部互连网络十分具有必要性。目前来看,绝大多数的高密度服务器均采用全互连网络以获得最优的网络性能。然而,全互连网络由于其节点度大,所以导致其成本高昂。在本文中,我们采用理论上最优的摩尔图来连接服务器内部的各个芯片。考虑到应用对计算能力的需求,我们选取节点规模为50的摩尔图,它被称为霍夫曼图。实际上,多个芯片将会被集成到一个处理器板上,这就意味着我们需要将原始的图划分为同构的连通子图。但是,现有的划分方法没有考虑上述问题因而产生异构的子图。为了解决这个问题,我们为霍夫曼图提出了两种等价划分机制。此外,我们提出了一种基于逻辑的最小路由机制,它能够节省时间和空间上的开销。最后,我们将提出的网络架构和全互连,Kautz和Torus这三种网络进行比较。实验结果表明我们提出的网络所达到的性能接近于全互连网络并且成本接近于Torus网络。

     

    Abstract: The high-density server is featured as low power, low volume, and high computational density. With the rising use of high-density servers in data-intensive and large-scale web applications, it requires a high-performance and cost-effcient intra-server interconnection network. Most of state-of-the-art high-density servers adopt the fully-connected intra-server network to attain high network performance. Unfortunately, this solution costs too much due to the high degree of nodes. In this paper, we exploit the theoretically optimized Moore graph to interconnect the chips within a server. Accounting for the suitable size of applications, a 50-size Moore graph, called Hoffman-Singleton graph, is adopted. In practice, multiple chips should be integrated onto one processor board, which means that the original graph should be partitioned into homogeneous connected subgraphs. However, the existing partition scheme does not consider above problem and thus generates heterogeneous subgraphs. To address this problem, we propose two equivalent-partition schemes for the Hoffman-Singleton graph. In addition, a logic-based and minimal routing mechanism, which is both time and area effcient, is proposed. Finally, we compare the proposed network architecture with its counterparts, namely the fully-connected, Kautz and Torus networks. The results show that our proposed network can achieve competitive performance as fully-connected network and cost close to Torus.

     

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