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dc.contributor.author
Vidal, Pablo Javier
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dc.contributor.author
Alba, Enrique
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dc.contributor.author
Luna, Francisco
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dc.date.available
2019-05-15T20:38:44Z
dc.date.issued
2017-06
dc.identifier.citation
Vidal, Pablo Javier; Alba, Enrique; Luna, Francisco; Solving optimization problems using a hybrid systolic search on GPU plus CPU; Springer Verlag; Soft Computing; 21; 12; 6-2017; 3227-3245
dc.identifier.issn
1433-7479
dc.identifier.uri
http://hdl.handle.net/11336/76482
dc.description.abstract
In recent years, graphics processing units (GPUs) have emerged as a powerful architecture for solving a broad spectrum of applications in very short periods of time. However, most existing GPU optimization approaches do not exploit the full power available in a CPU–GPU platform. They have a tendency to leave one of them partially unused (usually the CPU) and fail to establish an accurate exchange of information that could help solve the target problem efficiently. Thus, better performance is expected from devising a hybrid CPU–GPU parallel algorithm that combines the highly parallel stream processing power of GPUs with the higher power of multi-core architectures. We have developed a hybrid methodology to efficiently solve optimization problems. We use a hybrid CPU–GPU architecture, to benefit from running it, in parallel, on both the CPU and the GPU. Our experiments over a heterogeneous set of combinatorial optimization problems with increasing dimensionality show a time gain of up to 365 × in our proposal, while demonstrating high numerical accuracy. This work is intended to open up a new line of research that matches both architectures with new algorithms and cooperation techniques.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer Verlag
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Cpu&Ndash;Gpu Cooperative Algorithm
dc.subject
Gpgpu
dc.subject
Heterogeneous Architectures
dc.subject
Optimization
dc.subject
Parallel Hybrid Algorithms
dc.subject.classification
Ciencias de la Computación
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dc.subject.classification
Ciencias de la Computación e Información
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Solving optimization problems using a hybrid systolic search on GPU plus CPU
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-05-15T14:32:58Z
dc.journal.volume
21
dc.journal.number
12
dc.journal.pagination
3227-3245
dc.journal.pais
Alemania
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dc.journal.ciudad
Berlin
dc.description.fil
Fil: Vidal, Pablo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Patagonia Austral; Argentina
dc.description.fil
Fil: Alba, Enrique. Universidad de Málaga; España
dc.description.fil
Fil: Luna, Francisco. Universidad de Málaga; España
dc.journal.title
Soft Computing
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s00500-015-2005-x
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1007/s00500-015-2005-x
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