A parallel-computing algorithm for high-energy physics particle tracking and decoding using GPU architectures

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dc.contributor.author Fernández Declara, Plácido
dc.contributor.author Camporá, Daniel Hugo
dc.contributor.author García Blas, Francisco Javier
dc.contributor.author Vom Bruch, Dorothea
dc.contributor.author García Sánchez, José Daniel
dc.contributor.author Neufeld, Niko
dc.date.accessioned 2021-01-14T11:22:34Z
dc.date.available 2021-01-14T11:22:34Z
dc.date.issued 2019-07-01
dc.identifier.bibliographicCitation P. Fernandez Declara, D. H. Cámpora Pérez, J. Garcia-Blas, D. Vom Bruch, J. Daniel García and N. Neufeld, "A Parallel-Computing Algorithm for High-Energy Physics Particle Tracking and Decoding Using GPU Architectures," in IEEE Access, vol. 7, pp. 91612-91626, 2019
dc.identifier.issn 2169-3536
dc.identifier.uri http://hdl.handle.net/10016/31712
dc.description.abstract 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 10 9 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 7.4 × compared to the LHCb baseline.
dc.description.sponsorship This work was supported in part by the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme under Grant 724777 ``RECEPT,'' and in part by the Spanish MINISTERIO DE ECONOMÍA Y COMPETITIVIDAD though Project Grant TIN2016-79637-P TOWARDS UNIFICATION OF HPC AND BIG DATA PARADIGMS.
dc.language.iso eng
dc.publisher IEEE Xplore
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other CUDA
dc.subject.other GPGPU
dc.subject.other Track reconstruction
dc.subject.other Particle tracking
dc.subject.other Parallel programming
dc.title A parallel-computing algorithm for high-energy physics particle tracking and decoding using GPU architectures
dc.type research article
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1109/ACCESS.2019.2927261
dc.rights.accessRights open access
dc.relation.projectID Gobierno de España. TIN2016-79637-P
dc.relation.projectID info:eu-repo/grantAgreement/724777/RECEPT
dc.identifier.publicationfirstpage 91612
dc.identifier.publicationlastpage 91626
dc.identifier.publicationtitle IEEE Access
dc.identifier.publicationvolume 7
dc.identifier.uxxi AR/0000026586
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.affiliation.dpto UC3M. Departamento de Informática
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemas
dc.type.hasVersion VoR
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