Traditional tracking with Kalman Filter on parallel architectures

Giuseppe Cerati, Peter Elmer, Steven Lantz, Ian Macneill, Kevin McDermott, Dan Riley, Matevž Tadel, Peter Wittich, Frank Würthwein, Avi Yagil

Research output: Contribution to journalConference articlepeer-review

7 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, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The most common track finding techniques in use today are however those based on the Kalman Filter. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. We report the results of our investigations into the potential and limitations of these algorithms on the new parallel hardware.

Original languageEnglish (US)
Article number012057
JournalJournal of Physics: Conference Series
Issue number1
StatePublished - May 22 2015
Event16th International Workshop on Advanced Computing and Analysis Techniques in Physics Research: Bridging Disciplines, ACAT 2014 - Prague, Czech Republic
Duration: Sep 1 2014Sep 5 2014

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy


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