Version 1
: Received: 22 September 2024 / Approved: 23 September 2024 / Online: 24 September 2024 (05:05:56 CEST)
How to cite:
Okunola, A. GPU Acceleration Techniques for Analyzing the Photochemical Properties of Nanoparticles in Bioinformatics Frameworks. Preprints2024, 2024091777. https://doi.org/10.20944/preprints202409.1777.v1
Okunola, A. GPU Acceleration Techniques for Analyzing the Photochemical Properties of Nanoparticles in Bioinformatics Frameworks. Preprints 2024, 2024091777. https://doi.org/10.20944/preprints202409.1777.v1
Okunola, A. GPU Acceleration Techniques for Analyzing the Photochemical Properties of Nanoparticles in Bioinformatics Frameworks. Preprints2024, 2024091777. https://doi.org/10.20944/preprints202409.1777.v1
APA Style
Okunola, A. (2024). GPU Acceleration Techniques for Analyzing the Photochemical Properties of Nanoparticles in Bioinformatics Frameworks. Preprints. https://doi.org/10.20944/preprints202409.1777.v1
Chicago/Turabian Style
Okunola, A. 2024 "GPU Acceleration Techniques for Analyzing the Photochemical Properties of Nanoparticles in Bioinformatics Frameworks" Preprints. https://doi.org/10.20944/preprints202409.1777.v1
Abstract
The integration of nanoparticles in biomedical applications requires precise analysis of their photochemical properties. However, computational simulations of these properties are computationally intensive, hindering rapid advancement. This study explores the implementation of Graphics Processing Unit (GPU) acceleration techniques to enhance computational efficiency in analyzing nanoparticle photochemistry within bioinformatics frameworks.By leveraging GPU parallel processing capabilities, we developed optimized algorithms for photochemical property simulations, achieving significant speedup over traditional Central Processing Unit (CPU)-based methods. Our GPU-accelerated framework demonstrated substantial performance gains in computing photochemical properties, such as absorption spectra and energy transfer rates.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
