Performance Comparison of Parallel Sorting Algorithms on Homogeneous Cluster of Workstations

Lai Lai Win Kyi; Nay Min Tun

doi:10.4028/www.scientific.net/AMR.433-440.3900

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 433-440Performance Comparison of Parallel Sorting...

Performance Comparison of Parallel Sorting Algorithms on Homogeneous Cluster of Workstations

Abstract:

Sorting appears the most attention among all computational tasks over the past years because sorted data is at the heart of many computations. Sorting is of additional importance to parallel computing because of its close relation to the task of routing data among processes, which is an essential part of many parallel algorithms. Many parallel sorting algorithms have been investigated for a variety of parallel computer architectures. In this paper, three parallel sorting algorithms have been implemented and compared in terms of their overall execution time. The algorithms implemented are the odd-even transposition sort, parallel merge sort and parallel shell sort. Cluster of Workstations or Windows Compute Cluster has been used to compare the algorithms implemented. The C# programming language is used to develop the sorting algorithms. The MPI library has been selected to establish the communication and synchronization between processors. The time complexity for each parallel sorting algorithm will also be mentioned and analyzed.

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Periodical:

Advanced Materials Research (Volumes 433-440)

Pages:

3900-3904

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.433-440.3900

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Online since:

January 2012

Authors:

Lai Lai Win Kyi, Nay Min Tun

Keywords:

Cluster of Workstations, Parallel Computing, Parallel Sorting Algorithms, Performance Analysis

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References

[1] Kalim Qureshi: A Practical Performance Comparison of Parallel Sorting Algorithms on Homogeneous Network of Workstations (2005).

Google Scholar

[2] Mono project, Open Source Platform Based . NET http: www. mono-project. com (2004).

Google Scholar

[3] Kumar V., Grama, A, Gupta, A, Ka Karypis, G. (1994). Introduction to Parallel Computing-The Benijamin/Cummings Publishing Company, Inc. 2nd edn., (2003), in press.

Google Scholar

[4] E. Lusk. Programming with MPI on Clusters. In 3rd IEEE International Conference on Cluster Computing (CLUSTER'01), October 2001 (2001).

DOI: 10.1109/clustr.2001.960000

Google Scholar

[5] Groop, W., Lusk, E., Skjellum, A.: Using MPI: portable parallel programming with the message passing interface, MIT, Cambridge (1999).

DOI: 10.7551/mitpress/7056.001.0001

Google Scholar

[6] F. Meyer auf der Heide, A Wigderson, The Complexity of Parallel Sorting, SIMA Journal of Computing, 16, 1, pp.100-107, February 1999 (1999).

DOI: 10.1137/0216008

Google Scholar

[7] D. Bitton, D. Dewitt, D. K Hsiko, J. Menon, A Taxonomy of Parallel Sorting, ACM Computing Surveys, 16, 3, pp.287-318, September 1984 (1984).

DOI: 10.1145/2514.2516

Google Scholar