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Optimizing Distributed Deep Learning in Heterogeneous Computing Platforms for Remote Sensing Data Classification

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dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.authorRico Gallego, Juan Antonio
dc.contributor.authorCavallaro, Gabriele
dc.contributor.authorHaut, Juan M.
dc.contributor.orcidhttps://orcid.org/0000-0003-1030-3729
dc.contributor.orcidhttps://orcid.org/0000-0002-4264-7473
dc.contributor.orcidhttps://orcid.org/0000-0002-3239-9904
dc.contributor.orcidhttps://orcid.org/0000-0001-6701-961X
dc.coverage.spatialKuala Lumpur, Malaysia
dc.coverage.temporal2022-07-22
dc.date.accessioned2024-11-19T09:32:49Z
dc.date.available2024-11-19T09:32:49Z
dc.date.issued2022
dc.descriptionThe registered version of this article, first published in “Institute of Electrical and Electronics Engineers Inc, 2022", is available online at the publisher's website: IEEE, https://doi.org/10.1109/IGARSS46834.2022.9883762 La versión registrada de este artículo, publicado por primera vez en “Institute of Electrical and Electronics Engineers Inc, 2022", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/IGARSS46834.2022.9883762
dc.description.abstractApplications from Remote Sensing (RS) unveiled unique challenges to Deep Learning (DL) due to the high volume and complexity of their data. On the one hand, deep neural network architectures have the capability to automatically ex-tract informative features from RS data. On the other hand, these models have massive amounts of tunable parameters, re-quiring high computational capabilities. Distributed DL with data parallelism on High-Performance Computing (HPC) sys-tems have proved necessary in dealing with the demands of DL models. Nevertheless, a single HPC system can be al-ready highly heterogeneous and include different computing resources with uneven processing power. In this context, a standard data parallelism strategy does not partition the data efficiently according to the available computing resources. This paper proposes an alternative approach to compute the gradient, which guarantees that the contribution to the gradi-ent calculation is proportional to the processing speed of each DL model's replica. The experimental results are obtained in a heterogeneous HPC system with RS data and demon-strate that the proposed approach provides a significant training speed up and gain in the global accuracy compared to one of the state-of-the-art distributed DL framework.en
dc.description.versionversión publicada
dc.identifier.citationS. Moreno-Álvarez, M. E. Paoletti, J. A. Rico, G. Cavallaro and J. M. Haut, "Optimizing Distributed Deep Learning in Heterogeneous Computing Platforms for Remote Sensing Data Classification," IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 2726-2729, doi: 10.1109/IGARSS46834.2022.9883762.
dc.identifier.doihttps://doi.org/10.1109/IGARSS46834.2022.9883762
dc.identifier.isbn978-1-6654-2792-0
dc.identifier.issn2153-6996 | e2153-7003
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24422
dc.language.isoen
dc.publisherIEEE
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.congressInternational Geoscience and Remote Sensing Symposium (IGARSS)
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordstrainingen
dc.subject.keywordsdeep learningen
dc.subject.keywordscomputational modelingen
dc.subject.keywordsdistributed databasesen
dc.subject.keywordspredictive modelsen
dc.subject.keywordsparallel processingen
dc.subject.keywordsfeature extractionen
dc.titleOptimizing Distributed Deep Learning in Heterogeneous Computing Platforms for Remote Sensing Data Classificationen
dc.typeactas de congresoes
dc.typeconference proceedingsen
dspace.entity.typePublication
relation.isAuthorOfPublication3482d7bc-e120-48a3-812e-cc4b25a6d2fe
relation.isAuthorOfPublication.latestForDiscovery3482d7bc-e120-48a3-812e-cc4b25a6d2fe
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