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Barrel shifter-a close approximation to the completely connected network in supporting dynamic tree structured computations

机译:桶形移位器-在支持动态树状结构计算中非常接近于完全连接的网络

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High performance computing requires high quality load distribution of processes of a parallel application over processors in a parallel computer at runtime such that both maximum load and dilation are minimized. The performance of a simple randomized tree growing algorithm on the barrel shifter and the Illiac networks is studied in this paper. The algorithm spreads tree nodes by letting them to take random walks to neighboring processors. We develop recurrence relations that characterize expected loads on all processors. We find that the performance ratio of probabilistic dilation-1 tree embedding in the barrel shifter network with N processors (a network with node degree O(log N)) is very close to that in the completely connected network of the same size. However, the hypercube network, which also has node degree log N, does not have such a capability. As a matter of factor, even the Illiac network, which is a subnetwork of the barrel shifter, has an optimal asymptotic performance ratio.
机译:高性能计算需要在运行时在并行计算机中的处理器上对并行应用程序的进程进行高质量的负载分配,以使最大负载和扩展最小化。本文研究了一种简单的随机树生长算法在桶形移位器和Illiac网络上的性能。该算法通过让树节点随机漫游到相邻处理器来扩展树节点。我们开发了重复关系,以表征所有处理器上的预期负载。我们发现,在具有N个处理器的桶形移位器网络(节点度为O(log N)的网络)中,概率扩张1树嵌入的性能比与相同大小的完全连接的网络中的概率扩张非常接近。但是,也具有节点度日志N的超立方体网络不具有这种能力。作为一个因素,即使是Illiac网络(桶形移位器的子网络)也具有最佳的渐近性能比。

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