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@INPROCEEDINGS{Rinaldi:291319,
      author       = {Rinaldi, L. and Belgiovine, M. and Di Sipio, R. and
                      Gabrielli, A. and Negrini, M. and Semeria, F. and Sidoti, A.
                      and Tupputi, S. A. and Villa, M.},
      title        = {{GPGPU} for track finding in {H}igh {E}nergy {P}hysics},
      address      = {Hamburg},
      publisher    = {Deutsches Elektronen-Synchrotron, DESY},
      reportid     = {PUBDB-2015-05320, arXiv:1507.03074},
      pages        = {17-22},
      year         = {2015},
      abstract     = {The LHC experiments are designed to detect large amount of
                      physics events produced with a very high rate. Considering
                      the future upgrades, the data acquisition rate will become
                      even higher and new computing paradigms must be adopted for
                      fast data-processing: General Purpose Graphics Processing
                      Units (GPGPU) is a novel approach based on massive parallel
                      computing. The intense computation power provided by
                      Graphics Processing Units (GPU) is expected to reduce the
                      computation time and to speed-up the low-latency
                      applications used for fast decision taking. In particular,
                      this approach could be hence used for high-level triggering
                      in very complex environments, like the typical inner
                      tracking systems of the multi-purpose experiments at LHC,
                      where a large number of charged particle tracks will be
                      produced with the luminosity upgrade. In this article we
                      discuss a track pattern recognition algorithm based on the
                      Hough Transform, where a parallel approach is expected to
                      reduce dramatically the execution time.},
      month         = {Sep},
      date          = {2014-09-10},
      organization  = {GPU Computing in High-Energy Physics,
                       Pisa (Italy), 10 Sep 2014 - 12 Sep
                       2014},
      keywords     = {CERN LHC Coll (INSPIRE) / multiprocessor: graphics
                      (INSPIRE) / track data analysis (INSPIRE) / trigger
                      (INSPIRE) / mathematical methods (INSPIRE)},
      cin          = {L},
      cid          = {I:(DE-H253)L-20120731},
      pnm          = {899 - ohne Topic (POF3-899)},
      pid          = {G:(DE-HGF)POF3-899},
      experiment   = {EXP:(DE-588)4443767-5},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)15},
      eprint       = {1507.03074},
      howpublished = {arXiv:1507.03074},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:1507.03074;\%\%$},
      url          = {https://bib-pubdb1.desy.de/record/291319},
}