Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction

Modern computers are equipped with powerful computing engines like multicore processors and GPUs. The 3DEM community has rapidly adapted to this scenario and many software packages now make use of high performance computing techniques to exploit these devices. However, the implementations thus far a...

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Detalles Bibliográficos
Autores: Agulleiro, José-Ignacio, Vázquez, Francisco, Garzón, E. M., Fernández, José Jesús
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2012
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/379856
Acceso en línea:http://hdl.handle.net/10261/379856
Access Level:acceso abierto
Palabra clave:CPU
GPU
Hybrid computing
CPU–GPU co-processing
High performance computing
Electron tomography
Tomographic reconstruction
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spelling Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstructionAgulleiro, José-IgnacioVázquez, FranciscoGarzón, E. M.Fernández, José JesúsCPUGPUHybrid computingCPU–GPU co-processingHigh performance computingElectron tomographyTomographic reconstructionModern computers are equipped with powerful computing engines like multicore processors and GPUs. The 3DEM community has rapidly adapted to this scenario and many software packages now make use of high performance computing techniques to exploit these devices. However, the implementations thus far are purely focused on either GPUs or CPUs. This work presents a hybrid approach that collaboratively combines the GPUs and CPUs available in a computer and applies it to the problem of tomographic reconstruction. Proper orchestration of workload in such a heterogeneous system is an issue. Here we use an on-demand strategy whereby the computing devices request a new piece of work to do when idle. Our hybrid approach thus takes advantage of the whole computing power available in modern computers and further reduces the processing time. This CPU+GPU co-processing can be readily extended to other image processing tasks in 3DEM.Work partially supported by the Spanish Ministry of Science (TIN2008-01117) and J. Andalucia (P10-TIC-6002, P11-TIC-7176), in part financed by the European Reg. Dev. Fund (ERDF).Peer reviewedElsevierMinisterio de Ciencia e Innovación (España)Junta de AndalucíaEuropean CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252012info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Preprintinfo:eu-repo/semantics/submittedVersionapplication/pdfhttp://hdl.handle.net/10261/379856reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1016/j.ultramic.2012.02.003Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3798562026-05-22T06:33:51Z
dc.title.none.fl_str_mv Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction
title Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction
spellingShingle Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction
Agulleiro, José-Ignacio
CPU
GPU
Hybrid computing
CPU–GPU co-processing
High performance computing
Electron tomography
Tomographic reconstruction
title_short Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction
title_full Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction
title_fullStr Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction
title_full_unstemmed Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction
title_sort Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction
dc.creator.none.fl_str_mv Agulleiro, José-Ignacio
Vázquez, Francisco
Garzón, E. M.
Fernández, José Jesús
author Agulleiro, José-Ignacio
author_facet Agulleiro, José-Ignacio
Vázquez, Francisco
Garzón, E. M.
Fernández, José Jesús
author_role author
author2 Vázquez, Francisco
Garzón, E. M.
Fernández, José Jesús
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
Junta de Andalucía
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv CPU
GPU
Hybrid computing
CPU–GPU co-processing
High performance computing
Electron tomography
Tomographic reconstruction
topic CPU
GPU
Hybrid computing
CPU–GPU co-processing
High performance computing
Electron tomography
Tomographic reconstruction
description Modern computers are equipped with powerful computing engines like multicore processors and GPUs. The 3DEM community has rapidly adapted to this scenario and many software packages now make use of high performance computing techniques to exploit these devices. However, the implementations thus far are purely focused on either GPUs or CPUs. This work presents a hybrid approach that collaboratively combines the GPUs and CPUs available in a computer and applies it to the problem of tomographic reconstruction. Proper orchestration of workload in such a heterogeneous system is an issue. Here we use an on-demand strategy whereby the computing devices request a new piece of work to do when idle. Our hybrid approach thus takes advantage of the whole computing power available in modern computers and further reduces the processing time. This CPU+GPU co-processing can be readily extended to other image processing tasks in 3DEM.
publishDate 2012
dc.date.none.fl_str_mv 2012
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Preprint
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/379856
url http://hdl.handle.net/10261/379856
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1016/j.ultramic.2012.02.003
Sí
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.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869410290475466752
score 15,811543

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