PARALLELIZATION STRATEGIES ANALYSIS FOR SUPERVISED LEARNING
Snezhina Yanakieva
Pages: 293-300
Publié: 23 Sep 2021
Vues: 1,880
Téléchargements: 176
Résumé: The algorithms for supervised learning in artificial neural networks (ANN) require time and high computational power. As these algorithms gain popularity in a variety of domains, it is critical for them to run fast. Following a brief survey of the different dimensions of parallelism in ANN this paper analyses the performance comparison between different parallelization techniques to show the advantages and disadvantages of these strategies.
Mots-clés : parallel neural network, multilayer perceptron, back-propagation, bulk synchronous parallelism, computer cluster
Citer cet article : Snezhina Yanakieva. PARALLELIZATION STRATEGIES ANALYSIS FOR SUPERVISED LEARNING. Journal of International Scientific Publications: Materials, Methods & Technologies 15, 293-300 (2021). https://www.scientific-publications.net/en/article/1002220/
Retour au sommaire du volume
© 2026 The Author(s). This is an open access article distributed under the terms of the
Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This permission does not cover any third party copyrighted material which may appear in the work requested.
Disclaimer: The Publisher and/or the editor(s) are not responsible for the statements, opinions, and data contained in any published works. These are solely the views of the individual author(s) and contributor(s). The Publisher and/or the editor(s) disclaim any liability for injury to individuals or property arising from the ideas, methods, instructions, or products mentioned in the content.
