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A top-down processor allocation scheme for hypercube computers

机译:超立方体计算机的自顶向下处理器分配方案

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An efficient processor allocation policy is presented for hypercube computers. The allocation policy is called free list since it maintains a list of free subcubes available in the system. An incoming request of dimension k (2/sup k/ nodes) is allocated by finding a free subcube of dimension k or by decomposing an available subcube of dimension greater than k. This free list policy uses a top-down allocation rule in contrast to the bottom-up approach used by the previous bit-map allocation algorithms. This allocation scheme is compared to the buddy, gray code (GC), and modified buddy allocation policies reported for the hypercubes. It is shown that the free list policy is optimal in a static environment, as are the other policies, and it also gives better subcube recognition ability compared to the previous schemes in a dynamic environment. The performance of this policy, in terms of parameters such as average delay, system utilization, and time complexity, is compared to the other schemes to demonstrate its effectiveness. The extension of the algorithm for parallel implementation, noncubic allocation, and inclusion/exclusion allocation is also given.
机译:提供了一种针对超立方体计算机的有效处理器分配策略。分配策略称为空闲列表,因为它维护系统中可用的空闲子多维数据集列表。通过找到尺寸为k的自由子多维数据集或分解尺寸大于k的可用子多维数据集来分配尺寸为k(2 / sup k /个节点)的传入请求。与以前的位图分配算法所使用的自底向上方法相比,此自由列表策略使用自上而下的分配规则。将此分配方案与为超立方体报告的伙伴,格雷码(GC)和修改的伙伴分配策略进行了比较。结果表明,与其他策略相比,自由列表策略在静态环境中是最佳的,并且与动态环境中的先前方案相比,它还具有更好的子多维数据集识别能力。将该策略的性能(如平均延迟,系统利用率和时间复杂度等参数)与其他方案进行比较,以证明其有效性。还给出了并行执行,非三次分配以及包含/排除分配算法的扩展。

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