TY  - CONF
AU  - Rinaldi, L.
AU  - Belgiovine, M.
AU  - Di Sipio, R.
AU  - Gabrielli, A.
AU  - Negrini, M.
AU  - Semeria, F.
AU  - Sidoti, A.
AU  - Tupputi, S. A.
AU  - Villa, M.
TI  - GPGPU for track finding in High Energy Physics
IS  - arXiv:1507.03074
CY  - Hamburg
PB  - Deutsches Elektronen-Synchrotron, DESY
M1  - PUBDB-2015-05320
M1  - arXiv:1507.03074
SP  - 17-22
PY  - 2015
AB  - 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.
T2  - GPU Computing in High-Energy Physics
CY  - 10 Sep 2014 - 12 Sep 2014, Pisa (Italy)
Y2  - 10 Sep 2014 - 12 Sep 2014
M2  - Pisa, Italy
KW  - CERN LHC Coll (INSPIRE)
KW  - multiprocessor: graphics (INSPIRE)
KW  - track data analysis (INSPIRE)
KW  - trigger (INSPIRE)
KW  - mathematical methods (INSPIRE)
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)15
UR  - https://bib-pubdb1.desy.de/record/291319
ER  -