Parallel Sorting by Approximate Splitting for Multi-core Processors

2010 ◽  
Author(s):  
Wei Wu ◽  
Pin Wu ◽  
Weilie Shang ◽  
Youtong Fang
Keyword(s):  
Parallel Sorting

Communication-efficient parallel sorting integers sequence on multi-core cluster

2013 ◽  
Vol 33 (3) ◽  
pp. 821-824
Author(s):  
Qi KE ◽  
Cheng ZHONG ◽  
Qingyuan CHEN ◽  
Xiangyan LU
Keyword(s):  
Parallel Sorting ◽  
Core Cluster

A constant-time parallel sorting algorithm and its optical implementation

IEEE Micro ◽  
1995 ◽  
Vol 15 (3) ◽  
pp. 60-71 ◽  
Author(s):  
A. Louri ◽  
J.A. Hatch ◽  
Jongwhoa Na
Keyword(s):  
Constant Time ◽  
Sorting Algorithm ◽  
Parallel Sorting

Parallel sorting machines

1981 ◽  
Author(s):  
Leon E. Winslow ◽  
Yuan-Chieh Chow
Keyword(s):  
Parallel Sorting

A new parallel sorting algorithm based upon min-mid-max operations

1984 ◽  
Vol 24 (2) ◽  
pp. 187-195 ◽  
Author(s):  
S. S. Tseng ◽  
R. C. T. Lee
Keyword(s):  
Sorting Algorithm ◽  
Parallel Sorting

Analisis Kinerja Algoritma Quick Double Merge Sort Paralel Menggunakan openMP

2020 ◽  
Vol 11 (2) ◽  
pp. 95-102
Author(s):  
I Nyoman Aditya Yudiswara ◽  
Abba Suganda
Keyword(s):  
Parallel Algorithms ◽  
The Other ◽  
Sorting Algorithm ◽  
Data Parallelism ◽  
Parallel Sorting ◽  
Sequential Algorithms ◽  
Sorting Algorithms ◽  
Merge Sort ◽  
Sort Algorithm ◽  
Quick Sort

Processor technology currently tends to increase the number of cores more than increasing the clock speed. This development is very useful and becomes an opportunity to improve the performance of sequential algorithms that are only done by one core. This paper discusses the sorting algorithm that is executed in parallel by several logical CPUs or cores using the openMP library. This algorithm is named QDM Sort which is a combination of sequential quick sort algorithm and double merge algorithm. This study uses a data parallelism approach to design parallel algorithms from sequential algorithms. The data used in this study are the data that have not been sorted and also the data that has been sorted is integer type which is stored in advance in a file. The parameter measured to determine the performance of the QDM Sort algorithm is speedup. In a condition where a large amount of data is above 4096 and the number of threads in QDM Sort is the same as the number of logical CPUs, the QDM Sort algorithm has a better speedup compared to the other parallel sorting algorithms discussed in this study. For small amounts of data it is still better to use sequential sorting algorithm.

scholarly journals Scalable energy-efficient parallel sorting on a fine-grained many-core processor array

2020 ◽  
Vol 138 ◽  
pp. 32-47
Author(s):  
Aaron Stillmaker ◽  
Brent Bohnenstiehl ◽  
Lucas Stillmaker ◽  
Bevan Baas
Keyword(s):  
Energy Efficient ◽  
Processor Array ◽  
Parallel Sorting ◽  
Fine Grained ◽  
Many Core

Software Design Based on Using Ontologies and Algorithm Algebra

2021 ◽  
pp. 217-255
Keyword(s):  
Programming Language ◽  
Software Design ◽  
Program Design ◽  
Main Program ◽  
Parallel Sorting ◽  
Design And Synthesis ◽  
And Algebra ◽  
Algorithm Scheme ◽  
Subject Domains

This chapter proposes an approach to the automated development of programs based on the use of ontological facilities and algebra-algorithmic toolkit for design and synthesis of programs (IDS). The program design ontology, developed using Protégé system and represented in OWL format, includes concepts from various subject domains (sorting, meteorological forecasting, and other) intended for description of main program objects: data, functions, and relations between them. IDS toolkit generates the initial (skeleton) algorithm scheme based on its ontological description extracted from OWL file. The generated scheme is the basis of further design of the algorithm and synthesis of a program in a target programming language. The approach is illustrated by examples of developing parallel sorting, meteorological forecasting, and N-body simulation programs.

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