The GAP project - GPU for real-time applications in high energy physics and medical imaging

The GAP project aims at the deployment of Graphic Processing Units (GPU) in real-time applications, ranging from online event selection (trigger) in high-energy physics experiments to medical imaging reconstruction. The final goal of the project is to demonstrate that GPUs can have a positive impact in sectors different for rate, bandwidth, and computing intensity. The relevant aspects under study are the analysis of the total latency of the system, the optimization of the computational algorithms, and the integration with the data acquisition system. In this contribution we report on the application of GPUs for trigger selections in particle physics experiments, and for the reconstruction of medical images acquired by a nuclear magnetic resonance system. In particular we discuss how specific trigger algorithms can be naturally parallelized and thus benefit from the implementation on the GPU architecture, in terms of execution speed and complexity of the analyzed events. As a benchmark application we consider the trigger algorithms of two different particle physics experiment: NA62 and Atlas, two different combination of event complexity and processing latency requirements. The fast and parallel execution of the trigger algorithm can improve the resolution of the calculated relevant quantities, that will enrich the purity of the collected data sample. The stability of this solution for increasing complexity of the analyzed events is particularly relevant for its application in the upcoming physics experiment. Most of the future accelerator machine upgrades will push further the rate of data to be processed, hence the GPU can provide a feasible solution to maintain sustainable trigger rates. A similar approach can be applied to medical imaging, with particular reference to NMR scan reconstruction with the kurtosis diffusion method. This recently developed technique is based on computationally very intense algorithms performed thousands of times to reconstruct image properties with a good resolution. The implementation of this elaboration on GPUs can significantly reduce the processing time, making it suitable for the use in real-time diagnostic.