Speaker
Lorenzo Rinaldi
(BO)
Description
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 Graphical Processing Units (GPGPU) can be used in a novel approach based on massive parallel computing. The intense computation power provided by GPGPU is expected to reduce the computation time and speed-up fast decision taking and low-latency applications. In particular, this approach could be hence used for high-level triggering in very complex environments, like the typical inner track detectors of the LHC experiments, where a large amount of pile-up events overlaying the intersting physics processes are expected with the luminosity upgrade.
In this contribution we discuss two typical use-cases where a parallel approach is expected to reduce dramatically the execution time: a track pattern recognition algorithm based on the Hough transform and a trigger model based on track fitting.
Primary author
Lorenzo Rinaldi
(BO)
Co-authors
Alessandro Gabrielli
(BO)
Antonio Sidoti
(ROMA1)
Franco Semeria
(BO)
Matteo Negrini
(BO)
Mr
Mauro Belgiovine
(INFN Bologna)
Mauro Villa
(BO)
Dr
Riccardo Di Sipio
(BO)
