Stochastic optimization of GeantV code by use of genetic algorithms
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1088/1742-6596/898/4/042026 http://hdl.handle.net/11449/170484 |
Resumo: | GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) and handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. The goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem. |
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spelling |
Stochastic optimization of GeantV code by use of genetic algorithmsGeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) and handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. The goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem.Parallel Computing Center Sao Paulo State University (UNESP)CERN Route de MeyrinBhabha Atomic Research Centre (BARC)Fermilab MS234, P.O. Box 500Intel CorporationInstitute of Space SciencesParallel Computing Center Sao Paulo State University (UNESP)Universidade Estadual Paulista (Unesp)Route de MeyrinBhabha Atomic Research Centre (BARC)MS234Intel CorporationInstitute of Space SciencesAmadio, G. [UNESP]Apostolakis, J.Bandieramonte, M.Behera, S. P.Brun, R.Canal, P.Carminati, F.Cosmo, G.Duhem, L.Elvira, D.Folger, G.Gheata, A.Gheata, M.Goulas, I.Hariri, F.Jun, S. Y.Konstantinov, D.Kumawat, H.Ivantchenko, V.Lima, G.Nikitina, T.Novak, M.Pokorski, W.Ribon, A.Seghal, R.Shadura, O.Vallecorsa, S.Wenzel, S.2018-12-11T16:51:01Z2018-12-11T16:51:01Z2017-11-23info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectapplication/pdfhttp://dx.doi.org/10.1088/1742-6596/898/4/042026Journal of Physics: Conference Series, v. 898, n. 4, 2017.1742-65961742-6588http://hdl.handle.net/11449/17048410.1088/1742-6596/898/4/0420262-s2.0-850384314732-s2.0-85038431473.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Physics: Conference Series0,2410,241info:eu-repo/semantics/openAccess2023-10-06T06:02:40Zoai:repositorio.unesp.br:11449/170484Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-05-23T11:37:20.578478Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Stochastic optimization of GeantV code by use of genetic algorithms |
title |
Stochastic optimization of GeantV code by use of genetic algorithms |
spellingShingle |
Stochastic optimization of GeantV code by use of genetic algorithms Amadio, G. [UNESP] |
title_short |
Stochastic optimization of GeantV code by use of genetic algorithms |
title_full |
Stochastic optimization of GeantV code by use of genetic algorithms |
title_fullStr |
Stochastic optimization of GeantV code by use of genetic algorithms |
title_full_unstemmed |
Stochastic optimization of GeantV code by use of genetic algorithms |
title_sort |
Stochastic optimization of GeantV code by use of genetic algorithms |
author |
Amadio, G. [UNESP] |
author_facet |
Amadio, G. [UNESP] Apostolakis, J. Bandieramonte, M. Behera, S. P. Brun, R. Canal, P. Carminati, F. Cosmo, G. Duhem, L. Elvira, D. Folger, G. Gheata, A. Gheata, M. Goulas, I. Hariri, F. Jun, S. Y. Konstantinov, D. Kumawat, H. Ivantchenko, V. Lima, G. Nikitina, T. Novak, M. Pokorski, W. Ribon, A. Seghal, R. Shadura, O. Vallecorsa, S. Wenzel, S. |
author_role |
author |
author2 |
Apostolakis, J. Bandieramonte, M. Behera, S. P. Brun, R. Canal, P. Carminati, F. Cosmo, G. Duhem, L. Elvira, D. Folger, G. Gheata, A. Gheata, M. Goulas, I. Hariri, F. Jun, S. Y. Konstantinov, D. Kumawat, H. Ivantchenko, V. Lima, G. Nikitina, T. Novak, M. Pokorski, W. Ribon, A. Seghal, R. Shadura, O. Vallecorsa, S. Wenzel, S. |
author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Route de Meyrin Bhabha Atomic Research Centre (BARC) MS234 Intel Corporation Institute of Space Sciences |
dc.contributor.author.fl_str_mv |
Amadio, G. [UNESP] Apostolakis, J. Bandieramonte, M. Behera, S. P. Brun, R. Canal, P. Carminati, F. Cosmo, G. Duhem, L. Elvira, D. Folger, G. Gheata, A. Gheata, M. Goulas, I. Hariri, F. Jun, S. Y. Konstantinov, D. Kumawat, H. Ivantchenko, V. Lima, G. Nikitina, T. Novak, M. Pokorski, W. Ribon, A. Seghal, R. Shadura, O. Vallecorsa, S. Wenzel, S. |
description |
GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) and handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. The goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-11-23 2018-12-11T16:51:01Z 2018-12-11T16:51:01Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1088/1742-6596/898/4/042026 Journal of Physics: Conference Series, v. 898, n. 4, 2017. 1742-6596 1742-6588 http://hdl.handle.net/11449/170484 10.1088/1742-6596/898/4/042026 2-s2.0-85038431473 2-s2.0-85038431473.pdf |
url |
http://dx.doi.org/10.1088/1742-6596/898/4/042026 http://hdl.handle.net/11449/170484 |
identifier_str_mv |
Journal of Physics: Conference Series, v. 898, n. 4, 2017. 1742-6596 1742-6588 10.1088/1742-6596/898/4/042026 2-s2.0-85038431473 2-s2.0-85038431473.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of Physics: Conference Series 0,241 0,241 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
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Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
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1803045853238132736 |