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Sorting large data sets on a massively parallel system

机译:在大型并行系统上对大数据集进行排序

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This paper presents a performance study for many of today's popular parallel sorting algorithms. It is the first to present a comparative study on a large scale MIMD system. The machine, a Parsytec GCel, contains 1024 processors connected as a two-dimensional grid. To justify the experimental results, we develop a theoretical model to predict the performance in terms of communication and computation times. We get a very close relation between the experiments and the theoretical model as long as the edge congestion caused by the algorithms is predicted precisely. We compare: Bitonicsort, Shearsort, Gridsort, Samplesort, and Radixsort. Experiments were performed using random instances according to a well known benchmark problem. Results show that for the machine we used, Bitonicsort performs best for smaller numbers of keys per processor (2048) and Samplesort outperforms all other methods for larger instances.
机译:本文为今天的许多流行的并行分类算法提供了一种性能研究。它是第一个对大规模MIMD系统提供比较研究。该机器是Parsytec Gcel,包含1024个处理器,作为二维网格连接。为了证明实验结果,我们开发理论模型,以预测通信和计算时间的性能。我们在实验和理论模型之间获得非常密切的关系,只要预测到由算法引起的边缘拥塞就预测。我们比较:Bitonicsort,Shearsort,Gridsort,Samplesort和RadixSort。根据众所周知的基准问题使用随机实例进行实验。结果表明,对于我们使用的机器,Bitonicsort对每个处理器(> 2048)的较少数量的键执行最佳,并且Samplesort优于较大实例的所有其他方法。

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