<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Accelerating computational processes is paramount in numerical infrastructure development, particularly in applications such as the finite element method (FEM) and extensive calculations for simulating 3D processes in materials. In this work, we introduce a novel technique for computing the magnetostatic field of an ellipsoid particle, leveraging CUDA on a graphical card for parallel processing. The implementation on a GPU resulted in a remarkable 20-fold improvement in calculation speed. This achievement not only expedites research tasks, but also enables the exploration of larger and more intricate simulations, facilitating quicker model refinements and deeper insights into material behaviours under various conditions. The utilization of GPU computing aligns with the broader trend in scientific research and engineering, offering a versatile solution for diverse computational challenges beyond this specific task of magnetism. Overall, our work contributes to the ongoing effort to harness high-performance computing (HPC) technologies for accelerated and more efficient simulations in materials science and related fields.</p>
</abstract>

Accelerating computational processes is paramount in numerical infrastructure development, particularly in applications such as the finite element method (FEM) and extensive calculations for simulating 3D processes in materials. In this work, we introduce a novel technique for computing the magnetostatic field of an ellipsoid particle, leveraging CUDA on a graphical card for parallel processing. The implementation on a GPU resulted in a remarkable 20-fold improvement in calculation speed. This achievement not only expedites research tasks, but also enables the exploration of larger and more intricate simulations, facilitating quicker model refinements and deeper insights into material behaviours under various conditions. The utilization of GPU computing aligns with the broader trend in scientific research and engineering, offering a versatile solution for diverse computational challenges beyond this specific task of magnetism. Overall, our work contributes to the ongoing effort to harness high-performance computing (HPC) technologies for accelerated and more efficient simulations in materials science and related fields.

GPGPU Programming for Dipolar Field Calculation

“Alexandru Ioan Cuza” University, Research Center with Integrated Techniques for Atmospheric Aerosol Investigation in Romania

BULETINUL INSTITUTULUI POLITEHNIC DIN IAȘI. Secția Matematica. Mecanică Teoretică. Fizică

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"GPGPU Programming for Dipolar Field Calculation"}

Accelerating computational processes is paramount in numerical infrastructure development, particularly in applications such as the finite element method (FEM)...

GPGPU Programming for Dipolar Field Calculation

Pubblicato online: 27 gen 2024

Pagine: 7 - 18

Ricevuto: 14 nov 2023

Accettato: 08 gen 2024

DOI: https://doi.org/10.2478/bipmf-2024-0001

Parole chiave
CUDA, GPGPU programming, Magnetostatic Field

© 2024 Sebastian Pricop et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

GPGPU Programming for Dipolar Field Calculation

Pubblicato online: 27 gen 2024

Pagine: 7 - 18

Ricevuto: 14 nov 2023

Accettato: 08 gen 2024

DOI: https://doi.org/10.2478/bipmf-2024-0001

Parole chiaveCUDA, GPGPU programming, Magnetostatic Field

© 2024 Sebastian Pricop et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
CUDA, GPGPU programming, Magnetostatic Field