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Volume 37, Issue 5
Application of Computational Modelling to Particle Physics

Marco Barbone, Alexander Howard, Mihaly Novak, Wayne Luk, Georgi Gaydadjiev & Alex Tapper

DOI: 10.4208/cicp.OA-2024-0233

Commun. Comput. Phys., 37 (2025), pp. 1358-1382.

Published online: 2025-05

[An open-access article; the PDF is free to any online user.]

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  • Abstract

This study introduces a methodology for forecasting accelerator performance in Particle Physics algorithms. Accelerating applications can require significant engineering effort, prototyping and measuring the speedup that might finally result in disappointing accelerator performance. The proposed methodology involves performance modelling and forecasting, enabling the prediction of potential speedup, identification of promising acceleration candidates, prior to any significant programming investment. By predicting worst-case scenarios, the methodology assists developers in deciding whether an application can benefit from acceleration, thus optimising effort. A Monte Carlo simulation example demonstrates the effectiveness of the proposed methodology. The result shows that the methodology provides a reasonable estimate for GPUs and, in the context of FPGAs, the predictions are extremely accurate, within 2% of the realised execution time.

  • Keywords

High performance computing, Monte Carlo, FPGA acceleration, GPU Acceleration, performance modelling.

  • AMS Subject Headings

68U01

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{CiCP-37-1358, author = {Barbone , MarcoHoward , AlexanderNovak , MihalyLuk , WayneGaydadjiev , Georgi and Tapper , Alex}, title = {Application of Computational Modelling to Particle Physics}, journal = {Communications in Computational Physics}, year = {2025}, volume = {37}, number = {5}, pages = {1358--1382}, abstract = {

This study introduces a methodology for forecasting accelerator performance in Particle Physics algorithms. Accelerating applications can require significant engineering effort, prototyping and measuring the speedup that might finally result in disappointing accelerator performance. The proposed methodology involves performance modelling and forecasting, enabling the prediction of potential speedup, identification of promising acceleration candidates, prior to any significant programming investment. By predicting worst-case scenarios, the methodology assists developers in deciding whether an application can benefit from acceleration, thus optimising effort. A Monte Carlo simulation example demonstrates the effectiveness of the proposed methodology. The result shows that the methodology provides a reasonable estimate for GPUs and, in the context of FPGAs, the predictions are extremely accurate, within 2% of the realised execution time.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2024-0233}, url = {http://global-sci.org/intro/article_detail/cicp/24097.html} }
TY - JOUR T1 - Application of Computational Modelling to Particle Physics AU - Barbone , Marco AU - Howard , Alexander AU - Novak , Mihaly AU - Luk , Wayne AU - Gaydadjiev , Georgi AU - Tapper , Alex JO - Communications in Computational Physics VL - 5 SP - 1358 EP - 1382 PY - 2025 DA - 2025/05 SN - 37 DO - http://doi.org/10.4208/cicp.OA-2024-0233 UR - https://global-sci.org/intro/article_detail/cicp/24097.html KW - High performance computing, Monte Carlo, FPGA acceleration, GPU Acceleration, performance modelling. AB -

This study introduces a methodology for forecasting accelerator performance in Particle Physics algorithms. Accelerating applications can require significant engineering effort, prototyping and measuring the speedup that might finally result in disappointing accelerator performance. The proposed methodology involves performance modelling and forecasting, enabling the prediction of potential speedup, identification of promising acceleration candidates, prior to any significant programming investment. By predicting worst-case scenarios, the methodology assists developers in deciding whether an application can benefit from acceleration, thus optimising effort. A Monte Carlo simulation example demonstrates the effectiveness of the proposed methodology. The result shows that the methodology provides a reasonable estimate for GPUs and, in the context of FPGAs, the predictions are extremely accurate, within 2% of the realised execution time.

Barbone , MarcoHoward , AlexanderNovak , MihalyLuk , WayneGaydadjiev , Georgi and Tapper , Alex. (2025). Application of Computational Modelling to Particle Physics. Communications in Computational Physics. 37 (5). 1358-1382. doi:10.4208/cicp.OA-2024-0233
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