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
- Institución
- Universidad Nacional de Córdoba
- OAI Identificador
- oai:rdu.unc.edu.ar:11086/552462
Ver los metadatos del registro completo
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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:UNC2025-09-29T13:43:15Zoai: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:25722025-09-29 13:43:15.485Repositorio 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 |
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conferenceObject |
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publishedVersion |
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ISSN 2591-3522 http://hdl.handle.net/11086/552462 |
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ISSN 2591-3522 |
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http://hdl.handle.net/11086/552462 |
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eng |
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application/pdf |
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Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdoba |
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