Application of Computational Modelling to Particle Physics (Communications in Computational Physics, 2025)
This study introduces a methodology for forecasting accelerator performance in Particle Physics algorithms. Accelerating applications can require significantengineering effort, prototyping and measuring the speedup that might finally result indisappointing 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 programminginvestment. By predicting worst-case scenarios, the methodology assists developers indeciding whether an application can benefit from acceleration, thus optimising effort.A Monte Carlo simulation example demonstrates the effectiveness of the proposedmethodology. The result shows that the methodology provides a reasonable estimatefor GPUs and, in the context of FPGAs, the predictions are extremely accurate, within2% of the realised execution time.