March 2026

Conference Paper

Accelerating detector simulations with Celeritas: Profiling and performance optimizations

By:
Lund, Amanda; Esseiva, Julien; Johnson, Seth R; Biondo, Elliott D; Canal, Philippe; Evans, Thomas M; Hollenbeck, Hayden; Yung jun, Soon; Lima, Guilherme; Morgan, Ben; Castro Tognini, Stefano
Journal Name:
EPJ Web of Conferences
Page Number:
1292
Volume:
337
Publication Date:
March 12, 2026
Conference Name:
Conference on Computing in High Energy and Nuclear Physics (CHEP) 2024
Conference Location:
Krakow, Poland
Conference Sponsor:
CERN
View DOI Listing:
https://doi.org/10.1051/epjconf/202533701292

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.