Parallel Processing of Sorting and Searching Algorithms Comparative Study

Saad AL-Azzam and Mohammad Qatawneh

Modern Applied Science, 2018, vol. 12, issue 4, 143

Abstract: Recently, supercomputers structure and its software optimization have been popular subjects. Much of the software recently consumes a long period of time both to sort and search datasets, and thus optimizing these algorithms becomes a priority. In order to discover the most efficient sorting and searching algorithms for parallel processing units, one can compare CPU runtime as a performance index. In this paper, Quick, Bubble, and Merge sort algorithms have been chosen for comparison, as well as sequential and binary as search algorithms. Each one of the sort and search algorithms was tested in worst, average and best case scenarios. And each scenario was applied using multiple techniques (sequential, multithread, and parallel processing) on a various number of processors to spot differences and calculate speed up factor.The proposed solution aims to optimize the performance of a supercomputer focusing one-time efficiency; all tests were conducted by The IMAN1 supercomputer which is Jordan's first and fastest supercomputer.

Date: 2018
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ccsenet.org/journal/index.php/mas/article/download/72784/41067 (application/pdf)
https://ccsenet.org/journal/index.php/mas/article/view/72784 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibn:masjnl:v:12:y:2018:i:4:p:143

Access Statistics for this article

More articles in Modern Applied Science from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().