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 tha...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.展开更多
基金supported by the CRUK Convergence Science Centre at The Institute of Cancer Research,London,and Imperial College London(A26234)funding from the Cancer Research UK programme grant C33589/A19727.
文摘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.
