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An Optimal Time-Power Tradeoff for Sorting on a Mesh-Connected Computer with On-Chip Optics

机译:在带有片上光学器件的网状连接计算机上进行排序的最佳时间-功率折衷

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Energy consumption has become a critical factor constraining the design of massively parallel computers, necessitating the development of new models and energy-efficient algorithms. The primary component of on-chip energy consumption is data movement, and the mesh computer is a natural model of this, explicitly taking distance into account. Unfortunately the dark silicon problem increasingly constrains the number of bits which can be moved simultaneously. For sorting, standard mesh algorithms minimize time and total data movement, and hence constraining the mesh to use only half its processors at any instant must double the time. It is anticipated that on-chip optics will be used to minimize the energy needed to move bits, but they have constraints on their layout. In an abstract model, we show that a pyramidal layout and a new power-aware algorithm allows one to sort with only a square root increase in time as the fraction of processors simultaneously powered decreases. Furthermore, this layout is shown to be optimal in terms of the time-power tradeoff required for sorting. Previous algorithms assumed fully powered systems, hence pyramid sorting was of no interest since when fully powered they are no faster than the base mesh. Our results show asymptotic theoretical limits of computation and energy usage on a model which takes physical constraints and developing interconnection technology into account.?
机译:能源消耗已成为制约大规模并行计算机设计的关键因素,因此有必要开发新模型和高能效算法。片上能耗的主要组成部分是数据移动,而网状计算机是自然的模型,它明确考虑了距离。不幸的是,暗硅问题越来越多地限制了可以同时移动的位数。为了进行排序,标准的网格算法将时间和总数据移动减至最少,因此将网格限制为在任何时刻仅使用一半的处理器必须将时间加倍。可以预期,将使用片上光学器件来最大程度地降低移动钻头所需的能量,但是它们在布局上存在限制。在一个抽象模型中,我们显示出金字塔形布局和一种新的功耗感知算法可以使排序的时间仅随平方根的增加而增加,这是因为同时供电的处理器比例降低了。此外,就排序所需的时间-功率折衷而言,该布局显示为最佳。以前的算法假定系统为全功率系统,因此金字塔分类没有意义,因为全功率系统时金字塔排序不会比基本网格快。我们的结果表明,在考虑物理约束和开发互连技术的模型上,计算和能耗的渐近理论极限。

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