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A processor allocation strategy using cube coalescing in hypercube multicomputers

机译:超立方体多计算机中使用立方体合并的处理器分配策略

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We propose a new processor allocation strategy for hypercube multicomputers. The allocation strategy is called cube coalescing (CC) strategy since it uses a cube coalescing method to find an available subcube which is formed by the non-neighbored nodes. The main advantage of this strategy is that the nodes which do not contribute to form a requested subcube can be filtered out by coalescing, and thus the exhaustive search for possible subcubes can be avoided even in the worst case. It is theoretically shown that that proposed CC strategy is not only statistically optimal but also it has a complete subcube recognition capability. Extensive simulation is carried out to compare this strategy with the other strategies in terms of performance measures of interest such as processor utilization, completion time, and allocation/deallocation time. It is shown that the CC strategy gives better performance and the least allocation/deallocation time compared to the other schemes in a dynamic environment.
机译:我们为超立方体多计算机提出了一种新的处理器分配策略。分配策略称为多维数据集合并(CC)策略,因为它使用多维数据集合并方法来查找由非相邻节点形成的可用子多维数据集。该策略的主要优点是,可以通过合并来过滤掉无助于形成请求的子多维数据集的节点,因此即使在最坏的情况下也可以避免详尽搜索可能的子多维数据集。理论上表明,提出的CC策略不仅在统计上是最优的,而且具有完整的子立方体识别能力。进行了广泛的仿真,以根据感兴趣的性能度量(例如处理器利用率,完成时间和分配/取消分配时间)将该策略与其他策略进行比较。结果表明,与动态环境中的其他方案相比,CC策略具有更好的性能和最少的分配/解除分配时间。

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