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    ALICE HLT TPC Tracking of Pb-Pb Events on GPUs

    Rohr, David, Gorbunov, Sergey, Szostak, Artur, Kretz, Matthias, Kollegger, Thorsten, Breitner, Timo, Alt, Torsten
    Journal of Physics: Conference Series, Volume 396, Part 1, Pages 012044, 2012
    Cornell University
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    Title: ALICE HLT TPC Tracking of Pb-Pb Events on GPUs
    Author: Rohr, David; Gorbunov, Sergey; Szostak, Artur; Kretz, Matthias; Kollegger, Thorsten; Breitner, Timo; Alt, Torsten
    Subject: Physics - Instrumentation And Detectors
    Description: The online event reconstruction for the ALICE experiment at CERN requires processing capabilities to process central Pb-Pb collisions at a rate of more than 200 Hz, corresponding to an input data rate of about 25 GB/s. The reconstruction of particle trajectories in the Time Projection Chamber (TPC) is the most compute intensive step. The TPC online tracker implementation combines the principle of the cellular automaton and the Kalman filter. It has been accelerated by the usage of graphics cards (GPUs). A pipelined processing allows to perform the tracking on the GPU, the data transfer, and the preprocessing on the CPU in parallel. In order for CPU pre- and postprocessing to keep step with the GPU the pipeline uses multiple threads. A splitting of the tracking in multiple phases searching for short local track segments first improves data locality and makes the algorithm suited to run on a GPU. Due to special optimizations this course of action is not second to a global approach. Because of non-associative floating-point arithmetic a binary comparison of GPU and CPU tracker is infeasible. A track by track and cluster by cluster comparison shows a concordance of 99.999%. With current hardware, the GPU tracker outperforms the CPU version by about a factor of three leaving the processor still available for other tasks. Comment: 8 pages, 12 figures, contribution to CHEP2012 conference
    Is part of: Journal of Physics: Conference Series, Volume 396, Part 1, Pages 012044, 2012
    Identifier: 1712.09407 (ARXIV ID)

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    ALICE HLT TPC Tracking of Pb-Pb Events on GPUs

    Rohr, David, Gorbunov, Sergey, Szostak, Artur, Kretz, Matthias, Kollegger, Thorsten, Breitner, Timo, Alt, Torsten
    arXiv.org, Dec 26, 2017
    © ProQuest LLC All rights reserved, Engineering Database, Publicly Available Content Database, ProQuest Engineering Collection, ProQuest Technology Collection, ProQuest SciTech Collection, Materials Science & Engineering Database, ProQuest Central (new), ProQuest Central Korea, SciTech Premium Collection, Technology Collection, ProQuest Central Essentials, ProQuest One Academic, Engineering Collection (ProQuest)
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    Title: ALICE HLT TPC Tracking of Pb-Pb Events on GPUs
    Author: Rohr, David; Gorbunov, Sergey; Szostak, Artur; Kretz, Matthias; Kollegger, Thorsten; Breitner, Timo; Alt, Torsten
    Contributor: Alt, Torsten (pacrepositoryorg)
    Subject: Data Transfer (Computers) ; Algorithms ; Radiation Counters ; Particle Trajectories ; Cellular Automata ; Clusters ; Reconstruction ; Tracking ; Graphics Processing Units ; Microprocessors ; Floating Point Arithmetic ; Kalman Filters ; Central Processing Units–Cpus ; Instrumentation and Detectors
    Description: The online event reconstruction for the ALICE experiment at CERN requires processing capabilities to process central Pb-Pb collisions at a rate of more than 200 Hz, corresponding to an input data rate of about 25 GB/s. The reconstruction of particle trajectories in the Time Projection Chamber (TPC) is the most compute intensive step. The TPC online tracker implementation combines the principle of the cellular automaton and the Kalman filter. It has been accelerated by the usage of graphics cards (GPUs). A pipelined processing allows to perform the tracking on the GPU, the data transfer, and the preprocessing on the CPU in parallel. In order for CPU pre- and postprocessing to keep step with the GPU the pipeline uses multiple threads. A splitting of the tracking in multiple phases searching for short local track segments first improves data locality and makes the algorithm suited to run on a GPU. Due to special optimizations this course of action is not second to a global approach. Because...
    Is part of: arXiv.org, Dec 26, 2017
    Identifier: 2331-8422 (E-ISSN); 10.1088/1742-6596/396/1/012044 (DOI)