The GAP project: GPU applications for High Level Trigger and Medical Imaging

Matteo Bauce, Andrea Messina, Marco Rescigno, Stefano Giagu, Gianluca Lamanna, Massimiliano Fiorini
2015
The aim of the GAP project is the deployment of Graphic Processing Units in real-time applications, ranging from the online event selection (trigger) in High-Energy Physics 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 computational intensity. Most crucial aspects currently under study are the analysis of the total latency of the system, the algorithms optimisations, and the
more » ... tegration with the data acquisition systems. In this paper we focus on the application of GPUs in asynchronous trigger systems, employed for the high level trigger of LHC experiments. The benefit obtained from the GPU deployement is particularly relevant for the foreseen LHC luminosity upgrade where highly selective algorithms will be crucial to maintain a sustainable trigger rates with very high pileup. As a study case, we will consider the ATLAS experimental environment and propose a GPU implementation for a typical muon selection in a high-level trigger system. The aim of the GAP project is the deployment of Graphic Processing Units in real-time applications, ranging from the online event selection (trigger) in High-Energy Physics 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 computational intensity. Most crucial aspects currently under study are the analysis of the total latency of the system, the algorithms optimisations, and the integration with the data acquisition systems. In this paper we focus on the application of GPUs in asynchronous trigger systems, employed for the high level trigger of LHC experiments. The benefit obtained from the GPU deployement is particularly relevant for the foreseen LHC luminosity upgrade where highly selective algorithms will be crucial to maintain a sustainable trigger rates with very high pileup. As a study case, we will consider the ATLAS experimental environment and propose a GPU implementation for a typical muon selection in a high-level trigger system.
doi:10.3204/desy-proc-2014-05/1 fatcat:itmpu5ru3vex5oamv4ldjuja3y