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Exploring the performance–power–energy balance of low-power multicore and manycore architectures for anomaly detection in remote sensing
dc.contributor.author | León Navarro, Germán | |
dc.contributor.author | Molero, Jose M. | |
dc.contributor.author | Garzon, E.M. | |
dc.contributor.author | García, I. | |
dc.contributor.author | Plaza, Antonio | |
dc.contributor.author | Quintana-Orti, Enrique S. | |
dc.date.accessioned | 2016-05-19T07:54:19Z | |
dc.date.available | 2016-05-19T07:54:19Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | LEÓN, G., et al. Exploring the performance–power–energy balance of low-power multicore and manycore architectures for anomaly detection in remote sensing. The Journal of Supercomputing, 2015, vol. 71, no 5, p. 1893-1906. | ca_CA |
dc.identifier.issn | 0920-8542 | |
dc.identifier.issn | 1573-0484 | |
dc.identifier.uri | http://hdl.handle.net/10234/159795 | |
dc.description.abstract | In this paper, we perform an experimental study of the interactions between execution time (i.e., performance), power, and energy that occur in modern low-power architectures when executing the RX algorithm for detecting anomalies in hyperspectral images (i.e., signatures which are spectrally different from their surrounding data). We believe this is important because, for airborne and spaceborne remote sensing missions, power and/or energy can be in practice as relevant as performance. In this sense, this paper investigates whether several recent low-power multithreaded architectures, from ARM and NVIDIA, can be a practical alternative in this domain to a standard high-performance multicore processor, using the RX anomaly detector as a case study. | ca_CA |
dc.description.sponsorShip | This work has been funded by Grants from the Spanish Ministry of Science and Innovation (TIN2008-01117, TIN2011-23283, TIN2012-37483-C03-01/03 and AYA2011-29334-C02-02), Junta de Andalucia (P10-TIC-6002, P11-TIC7176, P12-TIC-301) and Junta de Extremadura (PRI09A110 and GR10035) in part financed by the European Regional Development Fund (ERDF). Moreover, this work has been developed in the framework of the network High Performance Computing on Heterogeneous Parallel Architectures (CAPAP-H4), supported by the Spanish Ministry of Science and Innovation (TIN2011-15734-E). | ca_CA |
dc.format.extent | 14 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer Verlag | ca_CA |
dc.relation.isPartOf | The Journal of Supercomputing, 2015, vol. 71, no 5 | ca_CA |
dc.rights | © Springer International Publishing AG, Part of Springer Science+Business Media | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | anomaly detection | ca_CA |
dc.subject | remote sensing | ca_CA |
dc.subject | power wall | ca_CA |
dc.subject | high performance | ca_CA |
dc.subject | multicore processors | ca_CA |
dc.subject | low-power architectures | ca_CA |
dc.title | Exploring the performance–power–energy balance of low-power multicore and manycore architectures for anomaly detection in remote sensing | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.1007/s11227-014-1372-x | |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | ca_CA |
dc.relation.publisherVersion | http://link.springer.com/article/10.1007%2Fs11227-014-1372-x | ca_CA |
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