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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Compiler Optimization Prediction with New Self-Improved Optimization Model

Author 1: Chaitali Shewale
Author 2: Sagar B. Shinde
Author 3: Yogesh B. Gurav
Author 4: Rupesh J. Partil
Author 5: Sandeep U. Kadam

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2023.0140267

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 2, 2023.

  • Abstract and Keywords
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Abstract: Users may now choose from a vast range of compiler optimizations. These optimizations interact in a variety of sophisticated ways with one another and with the source code. The order in which optimization steps are applied can have a considerable influence on the performance obtained. As a result, we created a revolutionary compiler optimization prediction model. Our model comprises three operational phases: model training, feature extraction, as well as model exploitation. The model training step includes initialization as well as the formation of candidate sample sets. The inputs were then sent to the feature extraction phase, which retrieved static, dynamic, and improved entropy features. These extracted features were then optimized by the feature exploitation phase, which employs an improved hunger games search algorithm to choose the best features. In this work, we used a Convolutional Neural Network to predict compiler optimization based on these selected characteristics, and the findings show that our innovative compiler optimization model surpasses previous approaches.

Keywords: Compiler optimization prediction; feature extraction; feature exploitation; improved hunger games search algorithm; convolutional neural network

Chaitali Shewale, Sagar B. Shinde, Yogesh B. Gurav, Rupesh J. Partil and Sandeep U. Kadam, “Compiler Optimization Prediction with New Self-Improved Optimization Model” International Journal of Advanced Computer Science and Applications(IJACSA), 14(2), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140267

@article{Shewale2023,
title = {Compiler Optimization Prediction with New Self-Improved Optimization Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140267},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140267},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {2},
author = {Chaitali Shewale and Sagar B. Shinde and Yogesh B. Gurav and Rupesh J. Partil and Sandeep U. Kadam}
}


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