Efficient Track Reconstruction on Modern Hardware

Abstract

Particle physics has become a massively data-intensive discipline. Huge particle accelerators — such as the Large Hadron Collider (LHC) at CERN — produce vast amounts of experimental data — 4 TB/s in the case of the LHCb experiment at CERN — which often must be processed in real time. Named after the b-quark, LHCb is one of the four big experiments at CERN. The general scope is to explain the matter/anti-matter asymmetry. The main focus is the study of particle decays involving beauty and charm quarks. In the LHCb Project, a continuous stream of hits is produced by the several stages of the LHCb detector. Given the low probability of observing an “interesting” collision, physicists produce a vast number of collision experiments in the hope of finding a few interesting ones. Thus, the event data have to be processed in real time, since there are no capabilities to store all collision event permanently with the current storage technology. Analyzing these data volumes has become the key limitation of the domain: any improvement in analysis performance translates into better insights on the physics side. In this report, we present the results of our experiments of our current work with the HybridSeeding track reconstruction algorithm.