Boosting materials science simulations by high performance computing

Autores
Millán, Emmanuel Nicolás; Ruestes, Carlos Javier; Wolovick, Nicolás; Bringa, Eduardo Marcial
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Ponencia presentada en el XXIII Congreso de Métodos Numéricos y sus Aplicaciones. La Plata, Argentina, del 7 al 10 de noviembre de 2017.
Fil: Millán, Emmanuel Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Millán, Emmanuel Nicolás. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil: Ruestes, Carlos Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Ruestes, Carlos Javier. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil: Wolovick, Nicolás. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Bringa, Eduardo Marcial. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Bringa, Eduardo Marcial. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.
Technology development is often limited by knowledge of materials engineering and manufacturing processes. This scenario spans across scales and disciplines, from aerospace engineering to MicroElectroMechanical Systems (MEMS) and NanoElectroMechanical Systems (NEMS). The mechanical response of materials is dictated by atomic/nanometric scale processes that can be explored by molecular dynamics (MD) simulations. In this work we employ atomistic simulations to prove indentation as a prototypical deformation process showing the advantage of High Performance Computing (HPC) implementations for speeding up research. Selecting the right HPC hardware for executing simulations is a process that usually involves testing different hardware architectures and software configurations. Currently, there are several alternatives, using HPC cluster facilities shared between several researchers, as provided by Universities or Government Institutions, owning a small cluster, acquiring a local workstation with a high-end microprocessor, and using accelerators such as Graphics Processing Units (GPU), Field Programmable Gate Arrays (FPGA), or Intel Many Integrated Cores (MIC). Given this broad set of alternatives, we run several benchmarks using various University HPC clusters, a former TOP500 cluster in a foreign computing center, two high-end workstations and several accelerators. A number of different metrics are proposed to compare the performance and aid in the selection of the best hardware architecture according to the needs and budget of researchers. Amongst several results, we find that the Titan X Pascal GPU has a ∼3 x speedup against 64 AMD Opteron CPU cores.
https://cimec.org.ar/ojs/index.php/mc/article/view/5277
Fil: Millán, Emmanuel Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Millán, Emmanuel Nicolás. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil: Ruestes, Carlos Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Ruestes, Carlos Javier. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil: Wolovick, Nicolás. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Bringa, Eduardo Marcial. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Bringa, Eduardo Marcial. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.
Ciencias de la Computación
Materia
High performance computing
Molecular dynamics simulations
Performance analysis
Accelerators
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Repositorio
Repositorio Digital Universitario (UNC)
Institución
Universidad Nacional de Córdoba
OAI Identificador
oai:rdu.unc.edu.ar:11086/552462
Acceder
id RDUUNC_667270b6a66993338643cbd15f1b7cd9
oai_identifier_str oai:rdu.unc.edu.ar:11086/552462
network_acronym_str RDUUNC
repository_id_str 2572
network_name_str Repositorio Digital Universitario (UNC)
spelling Boosting materials science simulations by high performance computingMillán, Emmanuel NicolásRuestes, Carlos JavierWolovick, NicolásBringa, Eduardo MarcialHigh performance computingMolecular dynamics simulationsPerformance analysisAcceleratorsPonencia presentada en el XXIII Congreso de Métodos Numéricos y sus Aplicaciones. La Plata, Argentina, del 7 al 10 de noviembre de 2017.Fil: Millán, Emmanuel Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Millán, Emmanuel Nicolás. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Ruestes, Carlos Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Ruestes, Carlos Javier. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Wolovick, Nicolás. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Bringa, Eduardo Marcial. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Bringa, Eduardo Marcial. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.Technology development is often limited by knowledge of materials engineering and manufacturing processes. This scenario spans across scales and disciplines, from aerospace engineering to MicroElectroMechanical Systems (MEMS) and NanoElectroMechanical Systems (NEMS). The mechanical response of materials is dictated by atomic/nanometric scale processes that can be explored by molecular dynamics (MD) simulations. In this work we employ atomistic simulations to prove indentation as a prototypical deformation process showing the advantage of High Performance Computing (HPC) implementations for speeding up research. Selecting the right HPC hardware for executing simulations is a process that usually involves testing different hardware architectures and software configurations. Currently, there are several alternatives, using HPC cluster facilities shared between several researchers, as provided by Universities or Government Institutions, owning a small cluster, acquiring a local workstation with a high-end microprocessor, and using accelerators such as Graphics Processing Units (GPU), Field Programmable Gate Arrays (FPGA), or Intel Many Integrated Cores (MIC). Given this broad set of alternatives, we run several benchmarks using various University HPC clusters, a former TOP500 cluster in a foreign computing center, two high-end workstations and several accelerators. A number of different metrics are proposed to compare the performance and aid in the selection of the best hardware architecture according to the needs and budget of researchers. Amongst several results, we find that the Titan X Pascal GPU has a ∼3 x speedup against 64 AMD Opteron CPU cores.https://cimec.org.ar/ojs/index.php/mc/article/view/5277Fil: Millán, Emmanuel Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Millán, Emmanuel Nicolás. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Ruestes, Carlos Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Ruestes, Carlos Javier. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Wolovick, Nicolás. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Bringa, Eduardo Marcial. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Bringa, Eduardo Marcial. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.Ciencias de la Computaciónhttps://orcid.org/0000-0002-5666-8355https://orcid.org/0000-0002-2764-1508https://orcid.org/0000-0002-1403-19542017info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfISSN 2591-3522http://hdl.handle.net/11086/552462enginfo:eu-repo/semantics/openAccessreponame:Repositorio Digital Universitario (UNC)instname:Universidad Nacional de Córdobainstacron:UNC2025-10-23T11:17:41Zoai:rdu.unc.edu.ar:11086/552462Institucionalhttps://rdu.unc.edu.ar/Universidad públicaNo correspondehttp://rdu.unc.edu.ar/oai/snrdoca.unc@gmail.comArgentinaNo correspondeNo correspondeNo correspondeopendoar:25722025-10-23 11:17:41.95Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdobafalse
dc.title.none.fl_str_mv Boosting materials science simulations by high performance computing
title Boosting materials science simulations by high performance computing
spellingShingle Boosting materials science simulations by high performance computing
Millán, Emmanuel Nicolás
High performance computing
Molecular dynamics simulations
Performance analysis
Accelerators
title_short Boosting materials science simulations by high performance computing
title_full Boosting materials science simulations by high performance computing
title_fullStr Boosting materials science simulations by high performance computing
title_full_unstemmed Boosting materials science simulations by high performance computing
title_sort Boosting materials science simulations by high performance computing
dc.creator.none.fl_str_mv Millán, Emmanuel Nicolás
Ruestes, Carlos Javier
Wolovick, Nicolás
Bringa, Eduardo Marcial
author Millán, Emmanuel Nicolás
author_facet Millán, Emmanuel Nicolás
Ruestes, Carlos Javier
Wolovick, Nicolás
Bringa, Eduardo Marcial
author_role author
author2 Ruestes, Carlos Javier
Wolovick, Nicolás
Bringa, Eduardo Marcial
author2_role author
author
author
dc.contributor.none.fl_str_mv https://orcid.org/0000-0002-5666-8355
https://orcid.org/0000-0002-2764-1508
https://orcid.org/0000-0002-1403-1954
dc.subject.none.fl_str_mv High performance computing
Molecular dynamics simulations
Performance analysis
Accelerators
topic High performance computing
Molecular dynamics simulations
Performance analysis
Accelerators
dc.description.none.fl_txt_mv Ponencia presentada en el XXIII Congreso de Métodos Numéricos y sus Aplicaciones. La Plata, Argentina, del 7 al 10 de noviembre de 2017.
Fil: Millán, Emmanuel Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Millán, Emmanuel Nicolás. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil: Ruestes, Carlos Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Ruestes, Carlos Javier. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil: Wolovick, Nicolás. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Bringa, Eduardo Marcial. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Bringa, Eduardo Marcial. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.
Technology development is often limited by knowledge of materials engineering and manufacturing processes. This scenario spans across scales and disciplines, from aerospace engineering to MicroElectroMechanical Systems (MEMS) and NanoElectroMechanical Systems (NEMS). The mechanical response of materials is dictated by atomic/nanometric scale processes that can be explored by molecular dynamics (MD) simulations. In this work we employ atomistic simulations to prove indentation as a prototypical deformation process showing the advantage of High Performance Computing (HPC) implementations for speeding up research. Selecting the right HPC hardware for executing simulations is a process that usually involves testing different hardware architectures and software configurations. Currently, there are several alternatives, using HPC cluster facilities shared between several researchers, as provided by Universities or Government Institutions, owning a small cluster, acquiring a local workstation with a high-end microprocessor, and using accelerators such as Graphics Processing Units (GPU), Field Programmable Gate Arrays (FPGA), or Intel Many Integrated Cores (MIC). Given this broad set of alternatives, we run several benchmarks using various University HPC clusters, a former TOP500 cluster in a foreign computing center, two high-end workstations and several accelerators. A number of different metrics are proposed to compare the performance and aid in the selection of the best hardware architecture according to the needs and budget of researchers. Amongst several results, we find that the Titan X Pascal GPU has a ∼3 x speedup against 64 AMD Opteron CPU cores.
https://cimec.org.ar/ojs/index.php/mc/article/view/5277
Fil: Millán, Emmanuel Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Millán, Emmanuel Nicolás. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil: Ruestes, Carlos Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Ruestes, Carlos Javier. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil: Wolovick, Nicolás. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Bringa, Eduardo Marcial. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Bringa, Eduardo Marcial. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina.
Ciencias de la Computación
description Ponencia presentada en el XXIII Congreso de Métodos Numéricos y sus Aplicaciones. La Plata, Argentina, del 7 al 10 de noviembre de 2017.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv ISSN 2591-3522
http://hdl.handle.net/11086/552462
identifier_str_mv ISSN 2591-3522
url http://hdl.handle.net/11086/552462
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.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 reponame:Repositorio Digital Universitario (UNC)
instname:Universidad Nacional de Córdoba
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reponame_str Repositorio Digital Universitario (UNC)
collection Repositorio Digital Universitario (UNC)
instname_str Universidad Nacional de Córdoba
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institution UNC
repository.name.fl_str_mv Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdoba
repository.mail.fl_str_mv oca.unc@gmail.com
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