Performance Evaluation of Parallel Algorithms

International Journal of Computer Science and Engineering
© 2022 by SSRG - IJCSE Journal
Volume 9 Issue 6
Year of Publication : 2022
Authors : Donald Ene, Vincent Ike Anireh

How to Cite?

Donald Ene, Vincent Ike Anireh, "Performance Evaluation of Parallel Algorithms," SSRG International Journal of Computer Science and Engineering , vol. 9,  no. 6, pp. 10-14, 2022. Crossref,


Evaluating how well a whole system or set of subsystems performs is one of the primary objectives of performance testing. We can tell via performance assessment if the architecture implementation meets the design objectives. Performance evaluations of several parallel algorithms are compared in this study. Both theoretical and experimental methods are used in performance assessment as a subdiscipline in computer science. The parallel method outperforms its sequential counterpart in terms of throughput. The parallel algorithm's performance (speedup) is examined, as shown in the result.


Algorithm, Performance, Parallel computing, Distributed systems.


[1] H Ammar et al., "Performance Evaluation of Parallel Algorithms and Architectures in Concurrent Multiprocessor Systems," 1988.
[Google Scholar] [Publisher Link]
[2] Sanjay Kumar Sharma, and Dr. Kusum Gupta, "Performance Analysis of Parallel Algorithms on the Multi-Core System Using OpenMP," International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), vol. 2, no. 5, pp. 55-64, 2012.
[Google Scholar] [Publisher Link]
[3] DR. Rajiv Chopra, "Advanced Computer Architecture," S. Chand Publishing, p. 544, 2013.
[4] S Aswin Karthik et al., "Design and Performance Evaluation of Parallel Algorithm for Topic Modelling," Anna University: Chennai, 2013.
[Google Scholar] [Publisher Link]
[5] Da Wang, Gauri Joshi, and Gregory Wornell, "Using Straggler Replication to Reduce Latency in Large-Scale Parallel Computing," ACM SIGMETRICS Performance Evaluation Review, vol. 43, no. 3, pp. 7-11, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Yanik Ngoko, and Denis, Trystram Scalability in Parallel Processing, Springer International Publishing AG, Part of Springer Nature, pp. 79-109, 2018.
[7] A.Y. Grama, "Isoefficiency: Measuring the Scalability of Parallel Algorithms and Architectures," IEEE Parallel and Distributed Technology: Systems and Applications, vol. 1, no. 3, pp. 12–21, 1993.
[CrossRef] [Google Scholar] [Publisher Link]
[8] R. G Brown, "Amdahl's Law and Parallel Speedup," Duke Trinity College of Arts and Sciences, Durham, 2000.
[9] Raymond Greenlaw, H. James Hoover, and Walter L. Ruzzo, "Limits to Parallel Computation: P-Completeness Theory," Oxford University Press, New York, NY, USA, 1995.
[Google Scholar]
[10] William A. Wulf, and Sally McKee, "Hitting the Memory Wall: Implications of the Obvious," ACM SIGARCH Computer Architecture News, vol. 23, no. 1, pp. 20–24, 1995.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Richard Brown et al, "Report to Congress on Server and Data Center Energy Efficiency: Public Law 109–431," Technical Report, Lawrence Berkeley National Laboratory, 2008.
[Publisher Link]
[12] J. L Gustafson, "Reevaluating Amdahl's Law," Communication of the ACM, vol. 31, no. 5, pp. 532–533.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Rahul Razdan, and Michael D. Smith, "A High-Performance Microarchitecture with Hardware-Programmable Functional Units," Proceedings of the 27th Annual International Symposium on Microarchitecture, pp. 172-180, 1994.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Pedro G. Coelho et al., “Parallel Computing Techniques Applied to the Simultaneous Design of Structure and Material,” Advances in Engineering Software, vol. 42, no. 5, pp. 219-227, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Kumar, V. P, and Gupta, A, "Analyzing Scalability of Parallel Algorithms and Architectures," Journal of Parallel and Distributed Computing, vol. 22, no. 3, pp. 379-391, 1994.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Loris Cannelli et al., "Asynchronous Parallel Algorithms for Nonconvex Optimization," Mathematical Programming, vol. 184, no. 1, pp. 121-154, 2020.
[CrossRef] [Google Scholar] [Publisher Link]