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Accelerating the RICH Particle Detector Algorithm on Intel Xeon Phi

At the LHC, particles are collided in order to understand how the universe was created. Those collisions are called events and generate large quantities of data, which have to be pre-filtered before they are stored to hard disks. This paper presents a parallel implementation of these algorithms that...

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Detalles Bibliográficos
Autores principales: Quast, Christina, Pohl, Angela, Cosenza, Biagio, Ben, Juurlink, Schwemmer, Rainer
Lenguaje:eng
Publicado: 2018
Materias:
Acceso en línea:https://dx.doi.org/10.1109/PDP2018.2018.00066
http://cds.cern.ch/record/2677501
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author Quast, Christina
Pohl, Angela
Cosenza, Biagio
Ben, Juurlink
Schwemmer, Rainer
author_facet Quast, Christina
Pohl, Angela
Cosenza, Biagio
Ben, Juurlink
Schwemmer, Rainer
author_sort Quast, Christina
collection CERN
description At the LHC, particles are collided in order to understand how the universe was created. Those collisions are called events and generate large quantities of data, which have to be pre-filtered before they are stored to hard disks. This paper presents a parallel implementation of these algorithms that is specifically designed for the Intel Xeon Phi Knights Landing platform, exploiting its 64 cores and AVX-512 instruction set. It shows that a linear speedup up until approximately 64 threads is attainable when vectorization is used, data is aligned to cache line boundaries, program execution is pinned to MCDRAM, mathematical expressions are transformed to a more efficient equivalent formulation, and OpenMP is used for parallelization. The code was transformed from being compute bound to memory bound. Overall, a speedup of 36.47x was reached while obtaining an error which is smaller than the detector resolution.
id oai-inspirehep.net-1689272
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2018
record_format invenio
spelling oai-inspirehep.net-16892722019-09-30T06:29:59Zdoi:10.1109/PDP2018.2018.00066http://cds.cern.ch/record/2677501engQuast, ChristinaPohl, AngelaCosenza, BiagioBen, JuurlinkSchwemmer, RainerAccelerating the RICH Particle Detector Algorithm on Intel Xeon PhiDetectors and Experimental TechniquesAt the LHC, particles are collided in order to understand how the universe was created. Those collisions are called events and generate large quantities of data, which have to be pre-filtered before they are stored to hard disks. This paper presents a parallel implementation of these algorithms that is specifically designed for the Intel Xeon Phi Knights Landing platform, exploiting its 64 cores and AVX-512 instruction set. It shows that a linear speedup up until approximately 64 threads is attainable when vectorization is used, data is aligned to cache line boundaries, program execution is pinned to MCDRAM, mathematical expressions are transformed to a more efficient equivalent formulation, and OpenMP is used for parallelization. The code was transformed from being compute bound to memory bound. Overall, a speedup of 36.47x was reached while obtaining an error which is smaller than the detector resolution.oai:inspirehep.net:16892722018
spellingShingle Detectors and Experimental Techniques
Quast, Christina
Pohl, Angela
Cosenza, Biagio
Ben, Juurlink
Schwemmer, Rainer
Accelerating the RICH Particle Detector Algorithm on Intel Xeon Phi
title Accelerating the RICH Particle Detector Algorithm on Intel Xeon Phi
title_full Accelerating the RICH Particle Detector Algorithm on Intel Xeon Phi
title_fullStr Accelerating the RICH Particle Detector Algorithm on Intel Xeon Phi
title_full_unstemmed Accelerating the RICH Particle Detector Algorithm on Intel Xeon Phi
title_short Accelerating the RICH Particle Detector Algorithm on Intel Xeon Phi
title_sort accelerating the rich particle detector algorithm on intel xeon phi
topic Detectors and Experimental Techniques
url https://dx.doi.org/10.1109/PDP2018.2018.00066
http://cds.cern.ch/record/2677501
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AT cosenzabiagio acceleratingtherichparticledetectoralgorithmonintelxeonphi
AT benjuurlink acceleratingtherichparticledetectoralgorithmonintelxeonphi
AT schwemmerrainer acceleratingtherichparticledetectoralgorithmonintelxeonphi