Accelerating detector simulations with Celeritas: Profiling and performance optimizations

¹ Argonne National Laboratory, Lemont, IL, USA
² Lawrence Berkeley National Laboratory, Berkeley, CA, USA
³ Oak Ridge National Laboratory, Oak Ridge, TN, USA
⁴ Fermi National Accelerator Laboratory, Batavia, IL, USA
⁵ University of Warwick, Coventry, United Kingdom
⁶ University of Virginia, Charlottesville, VA, USA

^* e-mail: alund@anl.gov

Published online: 7 October 2025

Abstract

Celeritas is a GPU-optimized Monte Carlo (MC) particle transport code designed to meet the growing computational demands of next-generation high energy physics (HEP) experiments. It provides efficient simulation of electromagnetic (EM) physics processes in complex geometries with magnetic fields, detector hit scoring, and seamless integration into Geant4-driven applications to offload EM physics to GPUs. Recent efforts have focused on performance optimizations and expanding profiling capabilities. This paper presents some key advancements, including the integration of the Perfetto system profiling tool for detailed performance analysis and the development of track-sorting methods to improve computational efficiency.