A parallel selection sorting algorithm on GPUs using binary search

S. Kumari, D. Singh
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引用次数: 10

Abstract

This paper describes a hybrid sorting which is the combination of radix sort and selection sort on graphic processing unit (GPU). The proposed algorithm is based on “Split and Concurrent Selection” (SCS) strategy. First, the data sequence is split in several pieces that are sorted in parallel using Radix sort. After that it applies parallel selection sort to obtain the final sorted sequence. Parallel selection sort finds the correct position of each elements of a data sequence and then copy the elements of a data sequence to corresponding position to obtain the final sorted data sequence. This paper analyses the computational complexity of proposed parallel sorting algorithm and compares it with other existing algorithms. It is implemented using CUDA 5.0 and results are evaluated on Tesla C2075 GPU. Experimental results of proposed algorithm are compared with results of best sequential sorting algorithm and odd- even merge sort based parallel sorting algorithm. Proposed algorithm shows up to 50 times speed up as compare to serial and two fold speedup as compare to parallel algorithm.
基于gpu的并行选择排序算法
本文介绍了一种在图形处理器(GPU)上结合基数排序和选择排序的混合排序方法。该算法基于“分割并发选择”(SCS)策略。首先,将数据序列分成几个部分,并使用基数排序并行排序。然后应用并行选择排序获得最终排序序列。并行选择排序找到数据序列中每个元素的正确位置,然后将数据序列中的元素复制到相应的位置,从而获得最终排序的数据序列。本文分析了所提出的并行排序算法的计算复杂度,并与现有算法进行了比较。使用CUDA 5.0实现,并在Tesla C2075 GPU上对结果进行了评估。实验结果与最佳顺序排序算法和基于奇偶归并排序的并行排序算法的结果进行了比较。该算法与串行算法相比,速度提高了50倍,与并行算法相比,速度提高了2倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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