A GPU-based Kalman Filter for Track Fitting

Understand

Computing centres, including those used to process High-Energy Physics data and simulations, are increasingly providing significant fractions of their computing resources through hardware architectures other than x86 CPUs, with GPUs being a common alternative.

• GPUs can provide excellent computational performance at a good price point for tasks that can be suitably parallelized.
• Charged particle (track) reconstruction is a computationally expensive component of HEP data reconstruction, and thus needs to use available resources in an efficient way.
• In this paper, an implementation of Kalman filter-based track fitting using CUDA and running on GPUs is presented.