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

We describe a pilot project for the use of GPUs (Graphics Processing Units) in online triggering applications for high energy physics experiments. Two major trends can be identified in the development of trigger and DAQ systems for particle physics experiments: the massive use of general-purpose commodity systems for data acquisition, such as commercial multicore PC farms, and the reduction of trigger levels implemented in hardware, aimed at a pure software selection system (trigger-less). The very innovative approach presented here aims at exploiting the parallel computing power of commercial GPUs to perform fast software-based computations both in early trigger stages and in high level triggers. General-purpose computing on GPUs is emerging as a new paradigm in several scientific fields. So far, however, GPU applications have only been tailored in order to accelerate offline computation. With the steady reduction of GPU latencies, and the increase in link and memory throughput, such devices have become mature for use in real-time applications in high energy physics data acquisition and trigger systems. We will discuss in detail the use of online parallel computing on GPUs for synchronous low level fixed-latency triggers. We will discuss the preliminary results of a first field test within the NA62 experiment at CERN. The use of GPUs in high level triggers will be also discussed. The ATLAS experiment at CERN, and in particular its muon trigger, will be taken as a case study for possible applications.