Evaluating Energy-Saving Strategies on Torus, K-Ary N-Tree, and Dragonfly

F. Zahn, Armin Schoffer, H. Fröning
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引用次数: 3

Abstract

Energy is one of the most crucial factors in the design of large-scale computing systems, especially high-performance computing. While exascale systems could be built with current hardware solutions, the required funding exceeds the budget of most institutions. Since a system is never fully utilized, energy-proportional components can save a substantial amount of energy. However, current interconnect technologies still operate at a fixed power consumption rate. Therefore, network power consumption becomes increasingly important as its contribution to overall power consumption is increasing. Energy-proportional interconnection networks is a research area that is still emerging. In this work, we analyze the effects of different topology characteristics on power consumption and potential energy savings of interconnection networks. We compare the differences in the design of common topologies and the related impact to energy savings. In particular, we analyze the power consumption of torus, k-ary n-tree, and dragonfly. We also use existing topology-independent power-saving policies to derive potential energy savings for each topology and compare the policies to other work which is specific to topology hardware features. The comparison concludes that topology-independent policies are superior for energy savings and the other work is superior for execution time.
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环面、K-Ary - n树和蜻蜓上的节能策略评价
能源是设计大型计算系统,尤其是高性能计算系统的关键因素之一。虽然百亿亿级系统可以用当前的硬件解决方案来构建,但所需的资金超过了大多数机构的预算。由于一个系统从来没有被充分利用,能量比例组件可以节省大量的能源。然而,目前的互连技术仍然以固定的功耗速率运行。因此,随着网络功耗对整体功耗的贡献越来越大,网络功耗变得越来越重要。能源比例互联网络是一个新兴的研究领域。在这项工作中,我们分析了不同的拓扑特性对互联网络的功耗和潜在节能的影响。我们比较了常见拓扑设计的差异及其对节能的相关影响。特别地,我们分析了环面、k-ary n-tree和蜻蜓的功耗。我们还使用现有的与拓扑无关的节能策略来推导每种拓扑的潜在节能,并将这些策略与特定于拓扑硬件特性的其他工作进行比较。通过比较得出,拓扑无关策略在节能方面更优,而其他策略在执行时间方面更优。
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Improving the Efficiency of Future Exascale Systems with rCUDA Evaluating Energy-Saving Strategies on Torus, K-Ary N-Tree, and Dragonfly VEF3 Traces: Towards a Complete Framework for Modelling Network Workloads for Exascale Systems Node-Type-Based Load-Balancing Routing for Parallel Generalized Fat-Trees Analyzing Topology Parameters for Achieving Energy-Efficient k-ary n-cubes
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