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

Aurelien Bailly-Reyre, Lingzhu Bian, Pierre Billoir, Daniel Hugo Campora Perez*, Vladimir Vava Gligorov*, Flavio Pisani, Renato Quagliani*, Alessandro Scarabotto*, Dorothea Vom Bruch

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Real-time data processing is a central aspect of particle physics experiments with high requirements on computing resources. The LHCb (Large Hadron Collider beauty) experiment must cope with the 30 million proton-proton bunches collision per second rate of the Large Hadron Collider (LHC), producing 109 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.

Original languageEnglish
Pages (from-to)114198-114211
Number of pages14
JournalIEEE Access
Volume12
DOIs
Publication statusPublished - 12 Aug 2024

Keywords

  • CUDA
  • GPU
  • track reconstruction
  • particle tracking
  • parallel programming

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