TY - JOUR
T1 - Genetic algorithm for data exchange optimization
AU - Awadalla, Medhat H.A.
N1 - Publisher Copyright:
© 2013 The Science and Information (SAI) Organization.
PY - 2019
Y1 - 2019
N2 - Dynamic architectures have emerged to be a promising implementation platform to provide flexibility, high performance, and low power consumption for computing devices. They can bring unique capabilities to computational tasks and offer the performance and energy efficiency of hardware with the flexibility of software. This paper proposes a genetic algorithm to develop an optimum configuration that optimizes the routing among its communicating processing nodes by minimizing the path length and maximizing possible parallel paths. In addition, this paper proposes forward, virtually inverse, and hybrid data exchange approaches to generate dynamic configurations that achieve data exchange optimization. Intensive experiments and qualitative comparisons have been conducted to show the effectiveness of the presented approaches. Results show significant performance improvement in terms of total execution time of up to 370%, 408%, 477%, and 550% when using configurations developed based on genetic algorithm, forward, virtually inverse, and hybrid data exchange techniques, respectively.
AB - Dynamic architectures have emerged to be a promising implementation platform to provide flexibility, high performance, and low power consumption for computing devices. They can bring unique capabilities to computational tasks and offer the performance and energy efficiency of hardware with the flexibility of software. This paper proposes a genetic algorithm to develop an optimum configuration that optimizes the routing among its communicating processing nodes by minimizing the path length and maximizing possible parallel paths. In addition, this paper proposes forward, virtually inverse, and hybrid data exchange approaches to generate dynamic configurations that achieve data exchange optimization. Intensive experiments and qualitative comparisons have been conducted to show the effectiveness of the presented approaches. Results show significant performance improvement in terms of total execution time of up to 370%, 408%, 477%, and 550% when using configurations developed based on genetic algorithm, forward, virtually inverse, and hybrid data exchange techniques, respectively.
KW - And hybrid data exchange method
KW - Dynamic architectures
KW - Forward data exchange
KW - Genetic algorithm
KW - Virtually inverse data exchange
UR - http://www.scopus.com/inward/record.url?scp=85063585553&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063585553&partnerID=8YFLogxK
U2 - 10.14569/ijacsa.2019.0100278
DO - 10.14569/ijacsa.2019.0100278
M3 - Article
AN - SCOPUS:85063585553
SN - 2158-107X
VL - 10
SP - 630
EP - 639
JO - International Journal of Advanced Computer Science and Applications
JF - International Journal of Advanced Computer Science and Applications
IS - 2
ER -