A Parallel-Computing Algorithm for High-Energy Physics Particle Tracking and Decoding Using GPU Architectures

Real-time data processing is one of the central processes of particle physics experiments which require large computing resources. The LHCb (Large Hadron Collider beauty) experiment will be upgraded to cope with a particle bunch collision rate of 30 million times per second, producing <inline-for...

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Bibliographic Details
Published in:IEEE access Vol. 7; pp. 91612 - 91626
Main Authors: Fernandez Declara, Placido, Campora Perez, Daniel Hugo, Garcia-Blas, Javier, Vom Bruch, Dorothea, Daniel Garcia, J., Neufeld, Niko
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accident reconstruction
Algorithms
Central processing units
Collision rates
Computer architecture
Configuration management
CPUs
CUDA
Data processing
Decoding
Detectors
GPGPU
Graphics processing units
Large Hadron Collider
parallel programming
Particle physics
Particle tracking
Particle trajectories
Physics
Real time
Real-time systems
track reconstruction
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Real-time data processing is one of the central processes of particle physics experiments which require large computing resources. The LHCb (Large Hadron Collider beauty) experiment will be upgraded to cope with a particle bunch collision rate of 30 million times per second, producing <inline-formula> <tex-math notation="LaTeX">10^{9} </tex-math></inline-formula> particles/s. 40 Tbits/s need to be processed in real-time to make filtering decisions to store data. This poses a computing challenge that requires exploration of modern hardware and software solutions. We present Compass , a particle tracking algorithm and a parallel raw input decoding optimized for GPUs. It is designed for highly parallel architectures, data-oriented, and optimized for fast and localized data access. Our algorithm is configurable, and we explore the trade-off in computing and physics performance of various configurations. A CPU implementation that delivers the same physics performance as our GPU implementation is presented. We discuss the achieved physics performance and validate it with Monte Carlo simulated data. We show a computing performance analysis comparing consumer and server-grade GPUs, and a CPU. We show the feasibility of using a full GPU decoding and particle tracking algorithm for high-throughput particle trajectories reconstruction, where our algorithm improves the throughput up to <inline-formula> <tex-math notation="LaTeX">7.4\times </tex-math></inline-formula> compared to the LHCb baseline.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2927261