Application of computational modelling to particle physics
File(s)
Published version
Author(s)
Type
Journal Article
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.
Date Issued
2025-05-01
Date Acceptance
2025-02-11
Citation
Communications in computational physics, 2025, 37 (5), pp.1358-1382
ISSN
1991-7120
Publisher
Global Science Press
Start Page
1358
End Page
1382
Journal / Book Title
Communications in computational physics
Volume
37
Issue
5
Copyright Statement
©2025 The Author(s). Published by Global-Science Press. Open Access. Licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.4208/cicp.OA-2024-0233
Subjects
AMS subject classifications: 68U01 High performance computing
Monte Carlo
FPGA acceleration
GPU Acceleration
performance modelling
Publication Status
Published