工程快速排序

S.Mansoor Sarwar , Syed Aqeel Sarwar , Mansour H.A. Jaragh , Jesse Brandeburg
{"title":"工程快速排序","authors":"S.Mansoor Sarwar ,&nbsp;Syed Aqeel Sarwar ,&nbsp;Mansour H.A. Jaragh ,&nbsp;Jesse Brandeburg","doi":"10.1016/0096-0551(96)00005-7","DOIUrl":null,"url":null,"abstract":"<div><p>This paper describes the results of a large empirical study to measure the run-time behavior of Quicksort by using various methods of computing the pivot element for medium to large size randomly generated integer data. The results of our study contradict the common notion that Quicksort gives best performance if median of three scheme is used to compute the pivot element and array partitions having &lt; 10 elements are sorted by using insertion sort. It was found that Quicksort performs best when median of three scheme is used to decide the pivot element and arrays with &lt; 4 elements are hand sorted. Our method gives an average speedup of &gt; 9% when compared to the method with a cutoff of 10 and sub-arrays with &lt; 10 elements insertion sorted for 1000 ⩽ <em>N</em> 1.5 × 10<sup>6</sup>. Our study shows that advanced hardware features allow for implementation of very fast codes for sorting small arrays, and using such codes instead of insertion sort can lead to substantial improvements for Quicksort, as conjectured by Sedgewick many years ago.</p></div>","PeriodicalId":100315,"journal":{"name":"Computer Languages","volume":"22 1","pages":"Pages 39-47"},"PeriodicalIF":0.0000,"publicationDate":"1996-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0096-0551(96)00005-7","citationCount":"14","resultStr":"{\"title\":\"Engineering quicksort\",\"authors\":\"S.Mansoor Sarwar ,&nbsp;Syed Aqeel Sarwar ,&nbsp;Mansour H.A. Jaragh ,&nbsp;Jesse Brandeburg\",\"doi\":\"10.1016/0096-0551(96)00005-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper describes the results of a large empirical study to measure the run-time behavior of Quicksort by using various methods of computing the pivot element for medium to large size randomly generated integer data. The results of our study contradict the common notion that Quicksort gives best performance if median of three scheme is used to compute the pivot element and array partitions having &lt; 10 elements are sorted by using insertion sort. It was found that Quicksort performs best when median of three scheme is used to decide the pivot element and arrays with &lt; 4 elements are hand sorted. Our method gives an average speedup of &gt; 9% when compared to the method with a cutoff of 10 and sub-arrays with &lt; 10 elements insertion sorted for 1000 ⩽ <em>N</em> 1.5 × 10<sup>6</sup>. Our study shows that advanced hardware features allow for implementation of very fast codes for sorting small arrays, and using such codes instead of insertion sort can lead to substantial improvements for Quicksort, as conjectured by Sedgewick many years ago.</p></div>\",\"PeriodicalId\":100315,\"journal\":{\"name\":\"Computer Languages\",\"volume\":\"22 1\",\"pages\":\"Pages 39-47\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/0096-0551(96)00005-7\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Languages\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/0096055196000057\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Languages","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/0096055196000057","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14

摘要

本文描述了一项大型实证研究的结果,该研究通过使用各种计算中大型随机生成的整数数据的枢轴元素的方法来测量快速排序的运行时行为。我们的研究结果与通常的观念相矛盾,即如果使用三种方案的中位数来计算主元素和具有<使用插入排序对10个元素排序。结果表明,采用三种方案的中位数来确定主元素和带<的数组时,快速排序效果最好;4个元素是手工排序的。我们的方法给出的平均加速为>与截止值为10的方法和带有<10个元素插入排序为1000≤N 1.5 × 106。我们的研究表明,先进的硬件特性允许实现非常快的代码来对小数组进行排序,并且使用这些代码代替插入排序可以导致快速排序的实质性改进,正如Sedgewick多年前所推测的那样。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Engineering quicksort

This paper describes the results of a large empirical study to measure the run-time behavior of Quicksort by using various methods of computing the pivot element for medium to large size randomly generated integer data. The results of our study contradict the common notion that Quicksort gives best performance if median of three scheme is used to compute the pivot element and array partitions having < 10 elements are sorted by using insertion sort. It was found that Quicksort performs best when median of three scheme is used to decide the pivot element and arrays with < 4 elements are hand sorted. Our method gives an average speedup of > 9% when compared to the method with a cutoff of 10 and sub-arrays with < 10 elements insertion sorted for 1000 ⩽ N 1.5 × 106. Our study shows that advanced hardware features allow for implementation of very fast codes for sorting small arrays, and using such codes instead of insertion sort can lead to substantial improvements for Quicksort, as conjectured by Sedgewick many years ago.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
State inference for dynamically changing interfaces LAILA: a language for coordinating abductive reasoning among logic agents Index to Volume 27, 2001 Argos: an automaton-based synchronous language Visual temporal logic as a rapid prototyping tool
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1