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DTSTAMP:20190719T085744Z
LOCATION:HG E 3
DTSTART;TZID=Europe/Stockholm:20190613T144500
DTEND;TZID=Europe/Stockholm:20190613T151500
UID:submissions.pasc-conference.org_PASC19_sess166_msa216@linklings.com
SUMMARY:Track Reconstruction Developments with GPUs
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Physi
 cs\n\nTrack Reconstruction Developments with GPUs\n\nCerati\n\nHigh energy
  physics experiments need to cope with increasing beam intensities, leadin
 g to higher event rates and larger pile-up. As a consequence, the event re
 construction time explodes and traditional techniques are not adequate to 
 select efficiently data with the online (trigger) applications nor to proc
 ess offline the large amount of collected data. The reconstruction of char
 ged particle trajectories is one of the most computationally challenging p
 roblems for event reconstruction in particle physics. At the High Luminosi
 ty LHC, for example, this will be by far the dominant problem. In the last
  decade, the emerging of highly parallel computing architectures represent
 ed a solution for the experiments to overcome the problem. Since then, man
 y developments targeted the application of reconstruction algorithms on su
 ch architectures, and some of them are already adopted by the experiments.
  Different approaches have been explored to exploit efficiently the new ar
 chitectures: re-engineering of existing algorithms, implementation of intr
 insically parallel algorithms, as well as deep learning techniques. The ra
 nge of interested experiments is also wide, including collision, fixed tar
 get, neutrino, and intensity frontier experiments. This presentation will 
 describe a few successful examples, their implementation on GPU architectu
 res, and discuss possible future directions of development.
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