Speaker
Description
At the HL-LHC, computing demands, particularly for event generation, will reach an unprecedented volume for which simple scaling of current resources will be insufficient, requiring new algorithmic and architectural strategies to sustain performance within economic and energy constraints.
A particularly promising approach is to identify parts of the simulation workflow that can be safely executed in single precision (FP32) without compromising physics accuracy. Even partial replacement of FP64 with FP32 can provide substantial improvements in both throughput and energy efficiency, with energy consumption commonly approximated to scale with the square of the number of significant bits. This consideration becomes even more critical as modern GPU architectures dedicate an increasing portion of their silicon to ultra-low-precision units originally designed for machine learning—such as FP16, FP8, and FP4—while FP64 performance improvements have largely plateaued.
In this study, we perform a detailed numerical validation of the MadGraph5 Monte Carlo event generator as an example simulation software package using stochastic arithmetic. By employing the CADNA and PROMISE frameworks, we automatically determine the minimally required precision across the code, rigorously identifying which sections require double precision (FP64) and which remain accurate in lower numerical formats.
The workflow presented is fully code-agnostic for any application written in C++, providing a general methodology for mixed-precision deployment. Our results outline a principled path toward exploiting future GPU architectures efficiently while preserving the numerical reliability.