Parallel Divide-and-Conquer Algorithms for Bubble Sort, Selection Sort and Insertion Sort

Ganapathi, Pramod; Chowdhury, Rezaul

doi:10.1093/comjnl/bxab107

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…Materials: The data used in our study came from a study entitled "Parallel Divide-and-Conquer Algorithm for Bubble Sort, Selection Sort, and Insertion Sort" by Pramod and Rezaul [17]. The data in the study were obtained by generating random numbers.…”

Section: )mentioning

confidence: 99%

Comparative Analysis of OpenMP and MPI Parallel Computing Implementations in Team Sort Algorithm

Nugroho,

Ashari,

Nashrullah

et al. 2023

JAIC

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Tim Sort is a sorting algorithm that combines Merge Sort and Binary Insertion Sort sorting algorithms. Parallel computing is a computational processing technique in parallel or is divided into several parts and carried out simultaneously. The application of parallel computing to algorithms is called parallelization. The purpose of parallelization is to reduce computational processing time, but not all parallelization can reduce computational processing time. Our research aims to analyse the effect of implementing parallel computing on the processing time of the Tim Sort algorithm. The Team Sort algorithm will be parallelized by dividing the flow or data into several parts, then each sorting and recombining them. The libraries we use are OpenMP and MPI, and tests are carried out using up to 16 core processors and data up to 4194304 numbers. The goal to be achieved by comparing the application of OpenMP and MPI to the Team Sort algorithm is to find out and choose which library is better for the case study, so that when there is a similar case, it can be used as a reference for using the library in solving the problem. The results of research for testing using 16 processor cores and the data used prove that the parallelization of the Sort Team algorithm using OpenMP is better with a speed increase of up to 8.48 times, compared to using MPI with a speed increase of 8.4 times. In addition, the increase in speed and efficiency increases as the amount of data increases. However, the increase in efficiency that is obtained by increasing the processor cores decreases.

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Section: )mentioning

confidence: 99%

Comparative Analysis of OpenMP and MPI Parallel Computing Implementations in Team Sort Algorithm

Nugroho,

Ashari,

Nashrullah

et al. 2023

JAIC

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“…There can be other parallel execution procedures as well for the optimization of graph analytics problems. (5) To make data science handle scalable data there are alternative methods as well, than that are proposed in this research work.…”

Section: Limitations Of the Proposalmentioning

confidence: 99%

Systematic Literature Review on Parallel Sorting and Searching Algorithms for Graph Analytics Problems

Kumar P A,

Rao Kunte,

Vaidya

2023

STAIQC

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This research paper aims to systematically review the literature published recently on the topic of parallel processing algorithms for sorting and searching methods. The paper emphasizesfinding the major gaps in the existing methods of using parallel sorting and searching methods for graph analytics problems and their use in scalable data science. Many peer-reviewed research papers and articles from international and national journals, conferences and other sources are searched using appropriate keywords and they are analyzed to understand the concept, current trend in the area and to find the research gaps, by employing a systematic review method of literature. The outcomes of this literature review haveresulted in the need to undertakea research work on "Design and Implementation of Parallel Sorting and Searching Algorithms for Graph Analytics Problems in Scalable Data Science". Also, we have identified the required research objectives and its plan of execution for the research proposal. Systematic literature review, Topic of research proposal along with research objectives, Listing of SWOT analysis of the research agendas, Listing of ABCD analysis of the research proposal

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“…Ref. [41] proposes a solution to reduce the running time and the number of swaps by the parallel realization and efficient use of memory. It presents an efficient parallel recursive divide-and-conquer algorithm applicable for Bubble, Selection, and Insertion sorting algorithms.…”

Section: Related Work and Why A New Framework Is Requiredmentioning

confidence: 99%

A General Framework for Sorting Large Data Sets Using Independent Subarrays of Approximately Equal Length

Moghaddam

2022

IEEE Access

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Designing an efficient data sorting algorithm that requires less time and space complexity is essential for computer science, different engineering disciplines, data mining systems, wireless networks, and the Internet of things. This paper proposes a general low-complex data sorting framework that distinguishes the sorted or similar data, makes independent subarrays approximately in equal length, and sorts the subarrays' data using one of the popular comparison-based sorting algorithms. Two frameworks, one for serial realization and another for parallel realization, are proposed. The time complexity analyses of the proposed framework demonstrate an improvement compared to the conventional Merge and Quick sorting algorithms. Following complexity analysis, the simulation results indicate slight improvements in the elapsed time and the number of swaps of the proposed serial Merge-based and Quick-based frameworks compared to the conventional ones for low/high variance integer/non-integer data sets, in different data sizes and the number of divisions. It is about (1−1.6%) to (3.5−4%) and (0.3−1.8%) to (2−4%) improvements in the elapsed times for 1, 2, 3, and 4 divisions, respectively for small and very large data sets in Mergebased and Quick-based scenarios. Although these improvements in serial realization are minor, making independent low-variance subarrays allows the sorted components to be extracted sequentially and gradually before the end of the sorting process. Also, it proposes a general framework for parallelizing conventional sorting algorithms using non-connected (independent) or connected (dependent) multi-core structures. As the second experiment, the numerical analyses that compare the results of the parallel realization of the proposed framework to the serial one in 1, 2, 3, and 4 divisions, show a speedup factor of (2 − 4) for small to (2 − 16) for very large data sets. The third experiment shows the effectiveness of the proposed parallel framework to the parallel sorting based on the random-access machine model. Finally, we prove that the mean-based pivot is as efficient as the median-based and much better than the random pivot for making subarrays of approximately equal length.

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Parallel Divide-and-Conquer Algorithms for Bubble Sort, Selection Sort and Insertion Sort

Cited by 5 publications

References 18 publications

Comparative Analysis of OpenMP and MPI Parallel Computing Implementations in Team Sort Algorithm

Comparative Analysis of OpenMP and MPI Parallel Computing Implementations in Team Sort Algorithm

Systematic Literature Review on Parallel Sorting and Searching Algorithms for Graph Analytics Problems

A General Framework for Sorting Large Data Sets Using Independent Subarrays of Approximately Equal Length

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