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An optimized bitonic sorting strategy with midpoint-based dynamic communication

机译:具有基于中点的动态通信的优化双音分类策略

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This paper proposes an optimized Bitonic sorting {OBS) strategy with midpoint-based dynamic communication. Our OBS strategy uses the midpoint-weight list ranking to improve complexity and reduce time of sorting on parallel and distributed systems. Applying a better key in the PE-list ranking can find the right place of (P., P,) and improve communication time significantly (i.e., fewer iterations, better synchronization in each iteration, faster convergence to the result), while most of coarse-grain parallel sorting (P < N) approaches improve only a large amount of data exchange (N/P) in each of static (s(s +1)/2) iterations. Theoretically, the OBS method can reduce fixed (s(s+1)/2) iterations to 1,2,3,..., ors = log_2 P iterations, which are improved over those (≤s(s +1)/2 iterations) of the dynamicDCES method. In performance evaluation, sorting was accomplished on multicore machines. Experimental results showed that our optimized OBS outperforms those of the dynamic DCES about 35%-40% and those of the static IBM about 51%-54% (forN = 10 to 100 million elements on an S-multicore computer).
机译:本文提出了一种基于中点的动态通信的优化的双子分类(OBS)策略。我们的OBS策略使用中点权重列表排名来提高复杂性并减少并行和分布式系统上排序的时间。在PE列表排名中应用更好的关键字可以找到(P.,P,)的正确位置,并显着缩短通信时间(即,迭代次数更少,每次迭代中的同步性更好,结果收敛更快),而大多数情况下粗粒度并行排序(P <N)方法在每次静态(s(s +1)/ 2)迭代中仅改善大量数据交换(N / P)。从理论上讲,OBS方法可以将固定(s(s + 1)/ 2)迭代减少为1,2,3,...,或ors = log_2 P迭代,相对于那些(≤s(s +1)/ dynamicDCES方法的2个迭代)。在性能评估中,排序是在多核计算机上完成的。实验结果表明,我们优化的OBS优于动态DCES的35%-40%,优于静态IBM的51%-54%(对于S多核计算机,N = 10到1亿个元素)。

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