Particle Flow Reconstruction on Heterogeneous Architecture for CMS

The Particle Flow (PF) algorithm, used for a majority of CMS data analyses for event reconstruction, provides a comprehensive list of final-state state particle candidates and enables efficient identification and mitigation methods for simultaneous proton-proton collisions (pileup). The higher instantaneous luminosity expected during the upcoming LHC Run 3 will impose challenges for CMS event reconstruction. This will be amplified in the HL-LHC era, where luminosity and pileup rates are expected to be significantly higher. One of the approaches CMS is taking to cope with this challenge is to adopt the heterogeneous computing architectures and accelerate event reconstruction. This note describes the effort to adopt some computation-intensive parts of PF reconstruction to take advantage of GPU accelerators, including the design and implementation of PF clustering for the CMS hadron calorimeter using CUDA. The physics validation and performance of the GPU-accelerated algorithms are demonstrated by comparing to the CPU-based implementation.