Deployment of a Matrix Element Method code for the ttH channel analysis on GPU’s platform

The observation of the associated production of the Higgs boson with two top quarks in proton-proton collisions is one of the highlights of the LHC Run 2. Driven by the theoretical description of the physics processes, the Matrix Element Method (MEM) consists in computing a probability that an event...

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Main Authors: Grasseau Gilles, Beaudette Florian, Perez Cristina Martin, Zabi Alexandre, Chiron Arnaud, Strebler Thomas, Hautreux Gabriel
Format: Article
Language:English
Published: EDP Sciences 2019-01-01
Series:EPJ Web of Conferences
Online Access:https://www.epj-conferences.org/articles/epjconf/pdf/2019/19/epjconf_chep2018_06028.pdf
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spelling doaj-6703605b4e78440490151fbf74a87ace2021-08-02T06:01:39ZengEDP SciencesEPJ Web of Conferences2100-014X2019-01-012140602810.1051/epjconf/201921406028epjconf_chep2018_06028Deployment of a Matrix Element Method code for the ttH channel analysis on GPU’s platformGrasseau GillesBeaudette FlorianPerez Cristina MartinZabi AlexandreChiron ArnaudStrebler ThomasHautreux GabrielThe observation of the associated production of the Higgs boson with two top quarks in proton-proton collisions is one of the highlights of the LHC Run 2. Driven by the theoretical description of the physics processes, the Matrix Element Method (MEM) consists in computing a probability that an event is compatible with the signal hypothesis (ttH) or with one of the background hypotheses. It is a powerful classifying tool requiring high dimensional integral computations. The deployment of our MEM production code on GPU’s platform will be described. What follows will focus on the adaptation of the main components of the computations in OpenCL kernels, namely the Magraph matrix element code generator, VEGAS, and LHAPDF. Finally, the gain obtained on GPU’s platforms compared with classical CPU’s platforms will be assessed.https://www.epj-conferences.org/articles/epjconf/pdf/2019/19/epjconf_chep2018_06028.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Grasseau Gilles
Beaudette Florian
Perez Cristina Martin
Zabi Alexandre
Chiron Arnaud
Strebler Thomas
Hautreux Gabriel
spellingShingle Grasseau Gilles
Beaudette Florian
Perez Cristina Martin
Zabi Alexandre
Chiron Arnaud
Strebler Thomas
Hautreux Gabriel
Deployment of a Matrix Element Method code for the ttH channel analysis on GPU’s platform
EPJ Web of Conferences
author_facet Grasseau Gilles
Beaudette Florian
Perez Cristina Martin
Zabi Alexandre
Chiron Arnaud
Strebler Thomas
Hautreux Gabriel
author_sort Grasseau Gilles
title Deployment of a Matrix Element Method code for the ttH channel analysis on GPU’s platform
title_short Deployment of a Matrix Element Method code for the ttH channel analysis on GPU’s platform
title_full Deployment of a Matrix Element Method code for the ttH channel analysis on GPU’s platform
title_fullStr Deployment of a Matrix Element Method code for the ttH channel analysis on GPU’s platform
title_full_unstemmed Deployment of a Matrix Element Method code for the ttH channel analysis on GPU’s platform
title_sort deployment of a matrix element method code for the tth channel analysis on gpu’s platform
publisher EDP Sciences
series EPJ Web of Conferences
issn 2100-014X
publishDate 2019-01-01
description The observation of the associated production of the Higgs boson with two top quarks in proton-proton collisions is one of the highlights of the LHC Run 2. Driven by the theoretical description of the physics processes, the Matrix Element Method (MEM) consists in computing a probability that an event is compatible with the signal hypothesis (ttH) or with one of the background hypotheses. It is a powerful classifying tool requiring high dimensional integral computations. The deployment of our MEM production code on GPU’s platform will be described. What follows will focus on the adaptation of the main components of the computations in OpenCL kernels, namely the Magraph matrix element code generator, VEGAS, and LHAPDF. Finally, the gain obtained on GPU’s platforms compared with classical CPU’s platforms will be assessed.
url https://www.epj-conferences.org/articles/epjconf/pdf/2019/19/epjconf_chep2018_06028.pdf
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