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Porting HEP Parameterized Calorimeter Simulation Code to GPUs

Journal Article · · Frontiers in Big Data
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The High Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), traditionally consume large amounts of CPU cycles for detector simulations and data analysis, but rarely use compute accelerators such as GPUs. As the LHC is upgraded to allow for higher luminosity, resulting in much higher data rates, purely relying on CPUs may not provide enough computing power to support the simulation and data analysis needs. As a proof of concept, we investigate the feasibility of porting a HEP parameterized calorimeter simulation code to GPUs. We have chosen to use FastCaloSim, the ATLAS fast parametrized calorimeter simulation. While FastCaloSim is sufficiently fast such that it does not impose a bottleneck in detector simulations overall, significant speed-ups in the processing of large samples can be achieved from GPU parallelization at both the particle (intra-event) and event levels; this is especially beneficial in conditions expected at the high-luminosity LHC, where extremely high per-event particle multiplicities will result from the many simultaneous proton-proton collisions. We report our experience with porting FastCaloSim to NVIDIA GPUs using CUDA. A preliminary Kokkos implementation of FastCaloSim for portability to other parallel architectures is also described.
Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
AC02-05CH11231; SC0012704
OSTI ID:
1812494
Alternate ID(s):
OSTI ID: 1827327
Report Number(s):
BNL-221925-2021-JAAM
Journal Information:
Frontiers in Big Data, Journal Name: Frontiers in Big Data Vol. 4; ISSN 2624-909X
Publisher:
FrontiersCopyright Statement
Country of Publication:
United States
Language:
English

References (5)

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journal July 2003
Kokkos: Enabling manycore performance portability through polymorphic memory access patterns journal December 2014
ATLAS data quality operations and performance for 2015–2018 data-taking journal April 2020
The ATLAS Experiment at the CERN Large Hadron Collider journal August 2008
The ATLAS Simulation Infrastructure journal September 2010

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Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
97 MATHEMATICS AND COMPUTING
CUDA
GPU
High Performance Computing
Kokkos
Large Hadron Collider
Particle Physics
Performance Portability
gpu
high performance computing
kokkos
large hadron collider
particle physics
performance portability