Kalman filter tracking on parallel architectures

Giuseppe Cerati, Peter Elmer, Slava Krutelyov, Steven Lantz, Matthieu Lefebvre, Kevin McDermott, Daniel Riley, Matevž Tadel, Peter Wittich, Frank Würthwein, Avi Yagil

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. In order to achieve the theoretical performance gains of these processors, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-LHC), for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are those based on a Kalman filter approach. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust, and are in use today at the LHC. Given the utility of the Kalman filter in track finding, we have begun to port these algorithms to parallel architectures, namely Intel Xeon and Xeon Phi. We report here on our progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a simplified experimental environment.

Original languageEnglish (US)
Article number00010
JournalEPJ Web of Conferences
StatePublished - Nov 15 2016
Event2016 Connecting the Dots - Vienna, Austria
Duration: Feb 22 2016Feb 24 2016

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy


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