Porting HEP Parameterized Calorimeter Simulation Code to GPUs

TL;DR

This paper explores porting the ATLAS FastCaloSim calorimeter simulation code to GPUs, demonstrating significant speed-ups and discussing implementation details for CUDA and portable parallel architectures like Kokkos.

Contribution

It presents the first successful port of a HEP parameterized calorimeter simulation to GPUs, enabling faster processing for high-luminosity LHC conditions.

Findings

GPU implementation achieves significant speed-ups

Porting to CUDA and Kokkos enhances performance and portability

Supports high particle multiplicities at high-luminosity LHC

Abstract

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.