Applications of GPUs to online track reconstruction in HEP experiments

Abstract

One of the most important issues that particle physics experiments at hadron colliders have to solve is realtime selection of the most interesting events. Typical collision frequencies do not allow all events to be written to tape for offline analysis, and in most cases, only a small fraction can be saved. The most commonly used strategy is based on two or three selection levels, with the low level ones usually exploiting dedicated hardware to decide within a few to ten microseconds if the event should be kept or not. This strict time requirement has made the usage of commercial devices inadequate, but recent improvements to Graphics Processing Units (GPUs) have substantially changed the conditions. Thanks to their highly parallel, multi-threaded, multicore architecture with remarkable computational power and high memory bandwidth, these commercial devices may be used in scientific applications, among which the event selection system (trigger) in particular may benefit, even at low levels. This paper describes the results of an R&D project to study the performance of GPU technology for low latency applications, such as HEP fast tracking trigger algorithms. On two different setups, we measure the latency to transfer data to/from the GPU, exploring the timing of different I/O technologies on different GPU models. We then describe the implementation and the performance of a track fitting algorithm which mimics the CDF Silicon Vertex Tracker. These studies provide performance benchmarks to investigate the potential and limitations of GPUs for future real-time applications in HEP experiments.