Speeding up Particle Track Reconstruction using a Parallel Kalman Filter Algorithm

TL;DR

This paper presents mkFit, a parallelized Kalman filter-based tracking algorithm optimized for SIMD architectures, achieving significant speedups while maintaining physics performance for particle track reconstruction at the HL-LHC.

Contribution

The paper introduces mkFit, a novel highly parallelized Kalman filter algorithm with a custom vectorization library, enabling faster particle tracking in high-performance computing environments.

Findings

Achieves a 6x speedup over the nominal algorithm.

Maintains comparable physics performance to existing CMS tracking methods.

Demonstrates effective scaling with increased parallel resources.

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 mkFit. A key piece of the algorithm is the Matriplex library, containing dedicated code to optimally vectorize operations on small matrices. The physics performance of the mkFit 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. mkFit 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.