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A fast recognition-complete processor allocation strategy for hypercube computers

机译:超立方体计算机的快速识别完成的处理器分配策略

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Fully recognizing various subcubes in a hypercube computer efficiently is addressed. A method with much less complexity than the multiple-GC strategy in generating the search space, while achieving complete subcube recognition, is proposed. This method is referred to as a dynamic processor allocation scheme because the search space generated is dependent on the dimension of the requested subcube dynamically. The basic idea lies in collapsing the binary tree representations of a hypercube successively so that the nodes which form a subcube but are distant are brought close to each other for recognition. The strategy can be implemented efficiently by using right rotating operations on the notations of the sets of subcubes corresponding to the nodes at a certain level of binary tree representations. Results of extensive simulation runs carried out to collect performance measures for different allocation strategies are discussed. It is shown that this strategy compares favorably in most situations with other known allocation schemes capable of achieving complete subcube recognition.
机译:解决了充分认识超立方体计算机中的各种子立方体的问题。提出了一种在生成搜索空间的同时实现完全子多维数据集识别的方法,其复杂度远低于多GC策略。此方法称为动态处理器分配方案,因为生成的搜索空间动态地取决于请求的子多维数据集的维度。基本思想在于连续折叠超多维数据集的二叉树表示,以便使形成子多维数据集但相距较远的节点彼此靠近以进行识别。通过在特定级别的二叉树表示形式上对与节点相对应的子多维数据集集的注释使用正确的旋转操作,可以有效地实施该策略。讨论了为收集不同分配策略的性能指标而进行的大量模拟运行的结果。结果表明,该策略在大多数情况下与能够实现完整子多维数据集识别的其他已知分配方案相比具有优势。

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