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.
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Notes
A spectral signature, or fingerprint, is the specific combination of emitted, reflected or absorbed electromagnetic radiation at varying wavelengths which can be leveraged to uniquely identify an object.
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Acknowledgments
We thank Francisco D. Igual, from Universidad Complutense de Madrid (Spain), for his help with the optimization and installation of BLAS for several of the platforms considered in this work. Last but not least, we gratefully thank the Associate Editor and the Anonymous Reviewers for their detailed comments and suggestions, which greatly helped us to improve the technical quality and presentation of our manuscript.
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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).
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León, G., Molero, J.M., Garzón, E.M. et al. Exploring the performance–power–energy balance of low-power multicore and manycore architectures for anomaly detection in remote sensing. J Supercomput 71, 1893–1906 (2015). https://doi.org/10.1007/s11227-014-1372-x
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DOI: https://doi.org/10.1007/s11227-014-1372-x