Looking Forward: A High-Throughput Track Following Algorithm for Parallel Architectures

Abstract

Real-time data processing is a central aspect of particle physics experiments with high requirements on computing resources. The LHCb experiment must cope with the 30 million proton-proton bunches collision per second rate of the Large Hadron Collider (LHC), producing $10^9$ particles/s. The large input data rate of 32 Tb/s needs to be processed in real time by the LHCb trigger system, which includes both reconstruction and selection algorithms to reduce the number of saved events. The trigger system is implemented in two stages and deployed in a custom data centre. We present Looking Forward, a high-throughput track following algorithm designed for the first stage of the LHCb trigger and optimised for GPUs. The algorithm focuses on the reconstruction of particles traversing the whole LHCb detector and is developed to obtain the best physics performance while respecting the throughput limitations of the trigger. The physics and computing performances are discussed and validated with simulated samples.

14 pages, 18 figures, to be published in IEEE Access

References in corpus (4)

• The CMS trigger system
• A Comparison of CPU and GPU implementations for the LHCb Experiment Run 3 Trigger
• A parallel-computing algorithm for high-energy physics particle tracking and decoding using GPU architectures
• Search by triplet: An efficient local track reconstruction algorithm for parallel architectures

Cited by in corpus (2)

• A Downstream and vertexing algorithm for Long Lived Particles (LLP) selection at the first High-level trigger (HLT1) of LHCb
• An Automated Bandwidth Division for the LHCb Upgrade Trigger