Researchers from all over the world are beginning to develop GPU algorithms for a variety of applications, including medicine, finance, chemistry, experimental and theoretical physics, network science and many more. This new branch of research is often referred to as \textbf{GPGPU}, i.e. General-Purpose computing on Graphics Processing Units, a term coined in 2002 which refers to an early trend of using GPUs for non-graphics applications \cite{Harris03real}.\\

High Energy Physics experiments rely on the acquisition and analysis of large amounts of information. Modern particle detectors feature very fast time responses, as required by the always increasing pace of accelerator technology. Most detectors feature a binned bi- or tri-dimensional geometry, providing accurate spatial information.\\

Such structured systems would natively benefit from parallel-oriented analysis techniques.
Moreover, particle physics computing is an intrinsically parallel problem: experiments produce lots of of events, which are computationally \textit{local}. Analysis is indeed carried out independently for each event on different memory locations.
Hence the use of \textit{multi-thread} techniques is expected to bring huge improvements in data processing.\\

Experiments such as ATLAS and ALICE at the LHC are starting to explore the advantages of using GPU (Graphical Processing Unit) programming in their \textit{High-Level Triggers} (HLT), in order to perform faster analysis on Terabytes of recorded data. In particular, a track selection algorithm was developed for ALICE, that combines a fast Cellular Automaton\footnote{Cellular automata are discrete deterministic mathematical models for scientific computation. An automaton consists of a grid of cells whose evolution is a function of the current state of the cell and its two immediate neighbors only.} method for pattern recognition and a Kalman Filter\footnote{A Kalman Filter is a statistical algorithm that produces an estimate of the system state by evaluating several streams of ``noisy'' data.} for track fitting \cite{5934702}. Kalman Filters are also used in ATLAS to speed up the process of vertex finding. ATLAS is also designing a parallelized version of a toolkit for Multi-Variate Analysis of events \cite{mckendrick2011analysis,washbrook2010algorithm}.\\

The Bejing Spectrometer (BES-III) experiment at BEPC, designed to perform a thorough study of the spectra of light hadrons, is successfully using a framework for partial wave analysis implemented on graphics processors \cite{2011arXiv1108.5673B}. Other ion and nucleon spectroscopy facilities such as FAIR (Facility for Antiproton and Ion Research at the GSI in Darmstadt) already make use of GPUs in their simulation and data analysis frameworks \cite{2011arXiv1106.6217N}.\\

A project is being developed within the NA62 collaboration, which aims to integrate GPUs into the lowest-level trigger for the first time in High Energy Physics research. The use of GPUs in such a hard real-time system has not been attempted so far, but it looks a realistic and challenging possibility.
The online use of GPUs would allow the computation of complex trigger primitives while providing a highly scalable trigger architecture. Emergent increased speed requirements would be handled just by adding more GPUs. Cards with hundreds of parallel floating-point units are extremely powerful hardware available at a relatively small cost, thanks to the continuous development driven by the huge market of computer games and by the broad excitement about GPGPU applications.