Speeding up Particle Track Reconstruction using a Parallel Kalman Filter Algorithm

Abstract

One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is determining the trajectory of charged particles during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filter-based methods for highly parallel, many-core SIMD architectures that are now prevalent in high-performance hardware. In this paper, we discuss the design and performance of the improved tracking algorithm, referred to as F . A key piece of the algorithm is the M library, containing dedicated code to optimally vectorize operations on small matrices. The physics performance of the F algorithm is comparable to the nominal CMS tracking algorithm when reconstructing tracks from simulated proton-proton collisions within the CMS detector. We study the scaling of the algorithm as a function of the parallel resources utilized and find large speedups both from vectorization and multi-threading.F achieves a speedup of a factor of 6 compared to the nominal algorithm when run in a single-threaded application within the CMS software framework. K: Data processing methods; Pattern recognition, cluster finding, calibration and fitting methods; Performance of High Energy Physics Detectors.