Call for Paper - July 2023 Edition
IJCA solicits original research papers for the July 2023 Edition. Last date of manuscript submission is June 20, 2023. Read More

Algorithm Analysis Tool based on Execution Time-Input Instance-based Runtime Performance Benchmarking

Print
PDF
 
Download Full Text
IJCA Proceedings on International Conference on Recent Developments in Science, Technology, Humanities and Management
© 2018 by IJCA Journal
ICRDSTHM 2017 - Number 1
Year of Publication: 2018
Authors:
Piyush Mishra
Vivek Patel
Parul Mittal
J. C. Patni
{bibtex}icrdsthm2017009.bib{/bibtex}

Abstract

Algorithms are a fundamental component of Computer Science, with every development in this field based on or around them. Each algorithm is evaluated for its performance using some technique, with asymptotic analysis being a frequently used one. Algorithms that have best time complexity theoretically (be it Oh, Theta or Omega Notation), may not have the best execution time in practice which depends on implementation efficacy, input dataset, constants and factors that are overlooked in asymptotic analysis. The lack of software which allows a user to compare various algorithms available for an operation for a given input dataset, supplemented with its graphical analysis encourages for the creation of the same. In this paper, we present a software tool which provides a range of algorithms for a given operation and measures the execution time for each of them. It then provides a graphical analysis of the algorithms executed, showing the performance of the algorithms belonging to a particular operation when run against a custom, input data set.

References

  • T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, 2001. Introduction to Algorithms, MIT Press.
  • Bernd Bischl, 2016. Aslib: A benchmark library for algorithm selection, Artificial Intelligence, Volume 237, pp. 41-58
  • www. research. ijcaonline. org/volume78/number14/pxc3891325. pdf.
  • Elizabeth D. Dolan, Jorge J. Moré, 2002. Benchmarking optimization software with performance profiles, Mathematical programming, pp. 201-213.
  • L. Barrett, A. Marathe, M. V. Marathe, D. Cook, G. Hicks, V. Faber, A. Srinivasan, Y. J. Sussmann, H. Thornquist, 2003. Statistical Analysis of Algorithms: A Case Study of Market-Clearing Mechanisms in the Power Industry, Journal of Graph Algorithms and Applications.
  • X. S. Yang, 2014. Nature-Inspired Optimization Algorithms, Elsevier.
  • Joe Zhu, 2014. Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets, Springer Volume 213.