  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.
