%0 Conference Paper
%A Rinaldi, L.
%A Belgiovine, M.
%A Di Sipio, R.
%A Gabrielli, A.
%A Negrini, M.
%A Semeria, F.
%A Sidoti, A.
%A Tupputi, S. A.
%A Villa, M.
%T GPGPU for track finding in High Energy Physics
%N arXiv:1507.03074
%C Hamburg
%I Deutsches Elektronen-Synchrotron, DESY
%M PUBDB-2015-05320
%M arXiv:1507.03074
%P 17-22
%D 2015
%X 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.
%B GPU Computing in High-Energy Physics
%C 10 Sep 2014 - 12 Sep 2014, Pisa (Italy)
Y2 10 Sep 2014 - 12 Sep 2014
M2 Pisa, Italy
%K CERN LHC Coll (INSPIRE)
%K multiprocessor: graphics (INSPIRE)
%K track data analysis (INSPIRE)
%K trigger (INSPIRE)
%K mathematical methods (INSPIRE)
%F PUB:(DE-HGF)8 ; PUB:(DE-HGF)15
%9 Contribution to a conference proceedingsInternal Report
%U https://bib-pubdb1.desy.de/record/291319