  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 an immense number of 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.
