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

Published on April 2018 by Piyush Mishra, Vivek Patel, Parul Mittal, J. C. Patni
journal_cover_thumbnail

International Conference on Recent Developments in Science, Technology, Humanities and Management
Foundation of Computer Science USA
ICRDSTHM2017 - Number 1
April 2018
Authors: Piyush Mishra, Vivek Patel, Parul Mittal, J. C. Patni
d39d6948-5d28-4505-b17f-8b0c18235324

Piyush Mishra, Vivek Patel, Parul Mittal, J. C. Patni . Algorithm Analysis Tool based on Execution Time-Input Instance-based Runtime Performance Benchmarking. International Conference on Recent Developments in Science, Technology, Humanities and Management. ICRDSTHM2017, 1 (April 2018), 27-30.

@article{
author = { Piyush Mishra, Vivek Patel, Parul Mittal, J. C. Patni },
title = { Algorithm Analysis Tool based on Execution Time-Input Instance-based Runtime Performance Benchmarking },
journal = { International Conference on Recent Developments in Science, Technology, Humanities and Management },
issue_date = { April 2018 },
volume = { ICRDSTHM2017 },
number = { 1 },
month = { April },
year = { 2018 },
issn = 0975-8887,
pages = { 27-30 },
numpages = 4,
url = { /proceedings/icrdsthm2017/number1/29311-7009/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Recent Developments in Science, Technology, Humanities and Management
%A Piyush Mishra
%A Vivek Patel
%A Parul Mittal
%A J. C. Patni
%T Algorithm Analysis Tool based on Execution Time-Input Instance-based Runtime Performance Benchmarking
%J International Conference on Recent Developments in Science, Technology, Humanities and Management
%@ 0975-8887
%V ICRDSTHM2017
%N 1
%P 27-30
%D 2018
%I International Journal of Computer Applications
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
  1. T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, 2001. Introduction to Algorithms, MIT Press.
  2. Bernd Bischl, 2016. Aslib: A benchmark library for algorithm selection, Artificial Intelligence, Volume 237, pp. 41-58
  3. www. research. ijcaonline. org/volume78/number14/pxc3891325. pdf.
  4. Elizabeth D. Dolan, Jorge J. Moré, 2002. Benchmarking optimization software with performance profiles, Mathematical programming, pp. 201-213.
  5. 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.
  6. X. S. Yang, 2014. Nature-Inspired Optimization Algorithms, Elsevier.
  7. Joe Zhu, 2014. Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets, Springer Volume 213.
Index Terms

Computer Science
Information Sciences

Keywords

Algorithms Benchmark Runtime-performance Graphical-analysis