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 - <p style="text-align: justify;">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.</p>