Real-time data processing with GPUs in high energy physics

Abstract

As high energy physics experiments reach higher luminosities and intensities, the computing burden for real time data processing and reduction grows. Following the developments in the computing landscape, multi-core processors such as graphics processing units (GPUs) are increasingly used for such tasks. These proceedings provide an introduction to the GPU architecture and describe how it maps to common tasks in real time data processing. In addition, specific use cases of GPUs in the trigger systems of five different high energy physics experiments are presented.

arXiv commentProceedings of the 5th INFIERI school, 11 pages, 2 figures, 2 tables

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• Allen: A high level trigger on GPUs for LHCb
• ALICE HLT TPC Tracking of Pb-Pb Events on GPUs
• Real-time data processing in the ALICE High Level Trigger at the LHC
• A New Three-Dimensional Track Fit with Multiple Scattering
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Cited by in corpus (6)

• GPU coprocessors as a service for deep learning inference in high energy physics
• A GPU-based Kalman Filter for Track Fitting
• Portable acceleration of CMS computing workflows with coprocessors as a service
• GPU Accelerated Automatic Differentiation With Clad
• Summary of the trigger systems of the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
• The SMARTHEP European Training Network