Speeding up particle track reconstruction using a parallel Kalman filter algorithm

S. Lantz, K. McDermott, M. Reid, D. Riley, P. Wittich, S. Berkman, G. Cerati, M. Kortelainen, A. Reinsvold Hall, P. Elmer, B. Wang, L. Giannini, V. Krutelyov, M. Masciovecchio, M. Tadel, F. Würthwein, A. Yagil, B. Gravelle, B. Norris

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Article numberP09030
JournalJournal of Instrumentation
Issue number9
StatePublished - Sep 2020

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Mathematical Physics


  • Calibration
  • Cluster finding
  • Fitting methods
  • Particle tracking detectors
  • Pattern recognition


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