Real-time data processing in the ALICE High Level Trigger at the LHC

Abstract

At the Large Hadron Collider at CERN in Geneva, Switzerland, atomic nuclei are collided at ultra-relativistic energies. Many final-state particles are produced in each collision and their properties are measured by the ALICE detector. The detector signals induced by the produced particles are digitized leading to data rates that are in excess of 48 GB/$s$. The ALICE High Level Trigger (HLT) system pioneered the use of FPGA- and GPU-based algorithms to reconstruct charged-particle trajectories and reduce the data size in real time. The results of the reconstruction of the collision events, available online, are used for high level data quality and detector-performance monitoring and real-time time-dependent detector calibration. The online data compression techniques developed and used in the ALICE HLT have more than quadrupled the amount of data that can be stored for offline event processing.

43 pages, 21 captioned figures, 3 tables, authors from page 38, published version, figures at http://alice-publications.web.cern.ch/node/4791

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• The ALICE TPC, a large 3-dimensional tracking device with fast readout for ultra-high multiplicity events
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Cited by in corpus (11)

• The ALICE experiment: a journey through QCD
• GPU coprocessors as a service for deep learning inference in high energy physics
• Usage of GPUs in ALICE Online and Offline processing during LHC Run 3
• Performance of the ALICE Electromagnetic Calorimeter
• Real-time data processing with GPUs in high energy physics
• Portable acceleration of CMS computing workflows with coprocessors as a service
• Data Flow in the Mu3e DAQ
• Overview of online and offline reconstruction in ALICE for LHC Run 3
• Usage of GPUs for online and offline Reconstruction in ALICE in Run 3
• Summary of the trigger systems of the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
• Deep Machine Learning for the PANDA Software Trigger