Investigating techniques to optimize data movement and reduce memory-related bottlenecks

Yiyang Hu

doi:10.54254/2755-2721/47/20241206

Research Article

Open access

Published on 15 March 2024

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Hu,Y. (2024). Investigating techniques to optimize data movement and reduce memory-related bottlenecks. Applied and Computational Engineering,47,81-86.

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Investigating techniques to optimize data movement and reduce memory-related bottlenecks

Yiyang Hu *^,1,

¹ The University of Essex

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/47/20241206

Abstract

In the ever-changing realm of computing, the importance of efficient data movement and the reduction of memory-related bottlenecks cannot be overstated. This research paper delves into a thorough examination of diverse methodologies and approaches aimed at optimizing data transfer and mitigating the constraints imposed by memory limitations. It offers an all-encompassing survey of pertinent literature, delving deep into techniques designed to enhance data movement efficiency, discussing effective strategies for alleviating memory bottlenecks, and presenting the outcomes of extensive experiments conducted. The findings of this study underscore the critical role played by these techniques in augmenting the performance, efficiency, and scalability of contemporary computing systems. In a world where the demand for computational power continues to grow, the ability to streamline data movement and overcome memory constraints is essential. By shedding light on these pivotal aspects of computing, this paper contributes to a more profound understanding of how to harness the full potential of modern computing systems, ultimately paving the way for groundbreaking advancements in the field.

Keywords

Data Movement Optimization, Memory-Related Bottlenecks, Computing Efficiency, Scalability Enhancement

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References

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Cite this article

Hu,Y. (2024). Investigating techniques to optimize data movement and reduce memory-related bottlenecks. Applied and Computational Engineering,47,81-86.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Conference website: https://www.confspml.org/

ISBN：978-1-83558-335-7(Print) / 978-1-83558-336-4(Online)

Conference date: 15 January 2024

Editor：Marwan Omar

Series: Applied and Computational Engineering

Volume number: Vol.47

ISSN：2755-2721(Print) / 2755-273X(Online)

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