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Parallel Optimization Strategy of Heap Sort Algorithm under Multi-core Environment

机译:多核环境下堆排序算法的并行优化策略

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With the popularity of multi-core and many-core platform, multi-core-oriented parallel programming and optimization has become a research hotspot in computer field. However, the vast majority of programmers are still continuing the traditional serial programming habits. Meanwhile, the mainstream algorithms are still in serial. Therefore, how to parallelize the serial programs effectively and write the multi-core programs efficiently is becoming an urgent problem in the field of multi-core programming. In this paper, the optimization of the heap sort algorithm has been achieved adopting the parallel technology based on openMP. Firstly, based on the hypercube model, the heap data to be sorted are divided into n (n is the computer thread amount) blocks called sub-heap which will be sorted respectively. Thus, the scale of the problem is decomposed into 1, secondly, the sorted sub-heaps will be merged using merge sort algorithm, finally, the parallel optimized heap sort algorithm is compared to the traditional heap sort algorithm. When the data quantity comes up to ten million bytes, the execution efficiency of the optimized parallel heap sort algorithm of this paper is improved significantly compared with the traditional serial heap sort algorithm on the experimental computer. Furthermore, the efficiency gap between the two algorithms will be intensified prominently with the increasing data quantity.
机译:随着多核和多核平台的普及,面向多核的并行编程和优化已成为计算机领域的研究热点。但是,绝大多数程序员仍在继续传统的串行编程习惯。同时,主流算法仍然是串行的。因此,如何有效地并行化串行程序并有效地编写多核程序成为多核程序设计领域的紧迫问题。本文采用基于openMP的并行技术,实现了堆排序算法的优化。首先,基于超多维数据集模型,将要排序的堆数据分为称为子堆的n个(n是计算机线程数量)块,这些块将分别进行排序。这样,问题的规模被分解为1 / n,其次,将使用合并排序算法对排序后的子堆进行合并,最后,将并行优化堆排序算法与传统堆排序算法进行了比较。当数据量达到一千万字节时,与实验计算机上的传统串行堆排序算法相比,本文优化的并行堆排序算法的执行效率得到了显着提高。此外,随着数据量的增加,两种算法之间的效率差距将显着加剧。

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