纵横多路并行归并算法

基于倾斜与振荡法多路归并排序算法,提出了纵横多路并行归并算法,与已有方法递归应用两路归并过程不同·该算法直接对m×k的矩阵(m,k为任意整数)进行排序,消除了对两路递归过程的依赖,是一种新的多路归并排序算法·通过和倾斜与振荡法多路归并排序算法和高效的任意路并行归并算法的性能分析比较,当3k40时,该算法的时间复杂性低于同类算法·同时,该算法在专用硬件实现的设计复杂性上也具有明显的优势·     

基于图形硬件的双调排序算法优化

介绍一种新的并行排序算法,该算法以双调归并排序为基础,运用图形硬件的并行体系结构和二叉排序树数据结构的优点,用部分并行代替所有阶段的顺序执行,对双调排序算法进行优化.对该算法进行分析,在理论上n个序列在P个流处理器上的排序,最优的时间复杂度为O((nlogn)/p).实验测试结果表明,优化后的算法比其它基于图形硬件的双调归并排序算法所用时间短.     

基于散列和归并技术的有效并行排序方法

本文提出一个在共享存储多处理机系统上实现的快速、有效的并行排序算法：将长度为ｎ的待排序数据划分成ｐ个长度为ｎ／ｐ的子序列，引入散列技术并行地对这ｐ个子序列的数据进行二次散列排序，这一阶段所需的平均时间为Ｏ（ｎ／ｐ）；最后并行地将ｐ个有序子序列归并成一个长度为ｎ的有序序列，归并阶段所需的时间为Ｏ（ｎ－ｎ／
／ｐ）。整个排序算法的并行执行代价为Ｏ（ｎｐ）。本排序方法可以拓以网络并行机群环境。     

基于流水光总线模型的快速归并排序算法

文章提出了一种LARPBS模型上的并行归并排序算法,利用该算法对长度为N的序列进行排序,使用N~(1+)着(0<着<1)个处理机可以在O((loglogN)~2)时间完成。     

一种基于MPP的并行归并算法

文中提出并分析了并行归并算法ＰＭＦＳ;基于曙光－１０００大规模并行计算机系统,给出了ＰＭＦＳ算法应用实例的实验结果,并将ＰＭＦＳ算法推广得到的并行归并排序算法与ＰＳＲＳ算法进行了比较。     

基于FPGA的KLT特征点多层次归并排序

KLT算法已在多个领域得到成功的应用,其中特征点的排序是用来选择好的特征点跟踪的关键。针对传统排序算法计算耗时、实时性差的缺点,提出一种可并行的多层次归并排序算法并在FPGA中实现了其并行计算,同时分析了其周期精确的计算时间。结果表明该归并排序算法可以[O(N)]的时间复杂度完成特征点的排序,能够满足高清分辨率的图像/视频数据中KLT特征点排序的实时性要求。     

带有宽总线网络的可重构计算模型上的并行归并排序算法

在介绍带有宽总线网络的可重构计算模型(RAPWBN)的二进制值的前缀和操作的基础上,提出了该模型上的抽取压缩操作算法,并由此得到了该模型上的并行归并排序算法。在具有N个处理器和N条行总线的RAPWBN模型上,若总线带宽ω>log N字节,对长度为N的序列进行归并排序,在最坏情况下以O(logN·loglogN)时间完成。     

一种归并排序算法的阵列映射

本文介绍一种归并排序算法－－插入归并算法的基本原理，并通过该算法的Ｓｙｓｔｏｌｉｃ阵列映射，重点阐述了正则映射生成ＶＬＳＩ阵列的理论和方法，最后，还指出了改进脉动阵列通用性和灵活性的途径。     

二路归并排序的链式实现方式

本文就《数据结构》课程中两路归并排序的算法的特点进行了分析,并提出了一个改进空间复杂性和时间复杂性的两路归并排序的算法。     

一种新的多种归并排序网络

文中提出了一种新的多种归并排序网络,该网络基于倾斜与振荡多路归并排序算法。     

A new external sorting algorithm with no additional disk space

This paper is concerned with an external sorting algorithm with no additional disk space. The proposed algorithm is a hybrid one that uses Quicksort and special merging process in two distinct phases. The algorithm excels in sorting a huge file, which is many times larger than the available memory of the computer. This algorithm creates no extra backup file for manipulating huge records. For this, the algorithm saves huge disk space, which is needed to hold the large file. Also our algorithm switches to special merging process after the first phase that uses Quicksort. This reduces the time complexity and makes the algorithm faster.     

A high‐performance sorting algorithm for multicore single‐instruction multiple‐data processors

Many sorting algorithms have been studied in the past, but there are only a few algorithms that can effectively exploit both single‐instruction multiple‐data (SIMD) instructions and thread‐level parallelism. In this paper, we propose a new high‐performance sorting algorithm, called aligned‐access sort (AA‐sort), that exploits both the SIMD instructions and thread‐level parallelism available on today's multicore processors. Our algorithm consists of two phases, an in‐core sorting phase and an out‐of‐core merging phase. The in‐core sorting phase uses our new sorting algorithm that extends combsort to exploit SIMD instructions. The out‐of‐core algorithm is based on mergesort with our novel vectorized merging algorithm. Both phases can take advantage of SIMD instructions. The key to high performance is eliminating unaligned memory accesses that would reduce the effectiveness of SIMD instructions in both phases. We implemented and evaluated the AA‐sort on PowerPC 970MP and Cell Broadband Engine platforms. In summary, a sequential version of the AA‐sort using SIMD instructions outperformed IBM's optimized sequential sorting library by 1.8 times and bitonic mergesort using SIMD instructions by 3.3 times on PowerPC 970MP when sorting 32 million random 32‐bit integers. Also, a parallel version of AA‐sort demonstrated better scalability with increasing numbers of cores than a parallel version of bitonic mergesort on both platforms. Copyright © 2011 John Wiley & Sons, Ltd.     

一种新的多路归并排序网络

文中提出了一种新的多路归并排序网络,该网络基于倾斜与振荡多路归并排序算法．该网络有两个主要特点．一是其基本构件为ｋ－ｓｏｒｔｅｒｓ,即ｋ个数的排序器,ｋ为任意素数,而传统的排序网络的基本构件为两个数的排序,即２－ｓｏｒｔｅｒｓ．二是该网络的延迟可以小于传统的基于２－ｓｏｒｔｅｒｓ的Ｂａｔｃｈｅｒ排序网络．文中给出了该排序网络的具体实现;作为实例给出了Ｎ＝２７,ｋ＝３时的排序网络;分析了该网络的时间延迟;通过具体设计排序网络的基本构件２－ｓｏｒｔｅｒｓ和３－ｓｏｒｔｅｒｓ,表明这种新的多路归并排序网络和Ｂａｔｃｈｅｒ排序网络相比是一种高速的排序网络．     

划分点定位并行排序算法

提出并分析了划分点定位并行排序（parallel sorting by divide-point locating)算法。在算法中，输入数据被平均划分并分配给所有处理机，因此每个处理机具有相同的工作负载。给出了网络分布计算环境下PSDL算法的实验结果，并与PSRS算法进行了对比。理论分析和实验结果表明，PSDL算法是一种高效率、高扩展性的并行排序算法。     

Sorting with near linear speed-up on tightly coupled multiprocessors

A new parallel sorting algorithm, called parsort, suitable for implementation on tightly coupled multiprocessors is presented. The algorithm is based upon quicksort and two-way merging. An asynchronous parallel partitioning algorithm is used to distribute work evenly during merging to ensure a good load balance amongst processors, which is crucial if we are to achieve high efficiency. The implementation of this parallel sorting algorithm exhibits theoretical and measured near linear speed-up when compared to sequential quicksort. This is illustrated by the results of experiments carried out on the Sequent Balance 8000 multiprocessor.     

Data parallel sorting for particle simulation

Sorting on a parallel architecture is a communications intensive event which can incur a high penalty in applications where it is required. In the case of particle simulation, only integer sorting is necessary, and sequential implementations easily attain the minimum performance bound of O(N) for N particles. Parallel implementations, however, have to cope with the parallel sorting problem which, in addition to incurring a heavy communications cost, can make the minimum performance bound difficult to attain. This paper demonstrates how the sorting problem in a particle simulation can be reduced to a merging problem, and describes an efficient data parallel algorithm to solve this merging problem in a particle simulation. The new algorithm is shown to be optimal under conditions usual for particle simulation, and its fieldwise implementation on the Connection Machine is analysed in detail. The new algorithm is about four times faster than a fieldwise implementation of radix sort on the Connection Machine.     

基于并行计算的大数据挖掘技术研究

大数据时代的到来催生了并行数据挖掘技术.本文介绍了大数据的基本概念,研究了Hadoop平台分布式程序设计模型MapReduce,并设计了并行数据挖掘中的并行分类算法和并行聚类算法.     

B-快速排序算法的研究与应用

描述了B-快速排序的基本思想，提出了正确的实现算法，然后给出了两种结构形式的、用C 实现的程序源代码，最后介绍了它在并行程序监测分析工具软件中的应用。     

THSORT:单机并行排序算法

排序是计算机事务处理的重要操作之一.前人已经就内部排序、外部排序和并行排序提出各种方法.从一种全新的视角研究了排序算法,提出一种在单机上实现的并行排序算法THSORT(Tsinghua SORT).它用多个进程分别控制不同的硬件部件,使输入、排序和输出能够同时进行,从而大大提高了硬件部件的并行性和运行效率.在带有双磁盘阵列的硬件平台上进行的测试表明,THSORT的性能达到了NTSORT(new technology SORT)的1倍左右,并成为2002年PennySort(Daytona类)世界排序纪录的保持者.     

Fast Four‐Way Parallel Radix Sorting on GPUs

Efficient sorting is a key requirement for many computer science algorithms. Acceleration of existing techniques as well as developing new sorting approaches is crucial for many real‐time graphics scenarios, database systems, and numerical simulations to name just a few. It is one of the most fundamental operations to organize and filter the ever growing massive amounts of data gathered on a daily basis. While optimal sorting models for serial execution on a single processor exist, efficient parallel sorting remains a challenge. In this paper, we present a hardware‐optimized parallel implementation of the radix sort algorithm that results in a significant speed up over existing sorting implementations. We outperform all known General Processing Unit (GPU) based sorting systems by about a factor of two and eliminate restrictions on the sorting key space. This makes our algorithm not only the fastest, but also the first general GPU sorting solution.