GPGPU for track finding in High Energy Physics

Rinaldi, L.; Sidoti, A.; Villa, M.; Di Sipio, R.; Tupputi, S. A.; Gabrielli, A.; Negrini, M.; Semeria, F.; Belgiovine, M.

Contribution to a conference proceedings/Internal Report

PUBDB-2015-05320

GPGPU for track finding in High Energy Physics

Rinaldi, L. ; Belgiovine, M. ; Di Sipio, R. ; Gabrielli, A. ; Negrini, M. ; Semeria, F. ; Sidoti, A. ; Tupputi, S. A. ; Villa, M.

2015
Deutsches Elektronen-Synchrotron, DESY Hamburg

GPU Computing in High-Energy Physics, GPUHEP2014, Pisa, Italy, 10 Sep 2014 - 12 Sep 2014 Hamburg : Deutsches Elektronen-Synchrotron, DESY 17-22 (2015)

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Report No.: arXiv:1507.03074

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.

Keyword(s): CERN LHC Coll ; multiprocessor: graphics ; track data analysis ; trigger ; mathematical methods