Modeling power consumption of 3D MPDATA and the CG method on ARM and Intel multicore architectures
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Other documents of the author: Rojek, Krzysztof; Quintana-Orti, Enrique S.; Wyrzykowski, Roman
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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Title
Modeling power consumption of 3D MPDATA and the CG method on ARM and Intel multicore architecturesDate
2017-03Publisher
Springer-VerlagBibliographic citation
Rojek, K., Quintana-Ortí, E. S., & Wyrzykowski, R. (2017). Modeling power consumption of 3D MPDATA and the CG method on ARM and Intel multicore architectures. The Journal of Supercomputing, 73(10), 4373-4389.Type
info:eu-repo/semantics/articlePublisher version
https://link.springer.com/article/10.1007/s11227-017-2020-zVersion
info:eu-repo/semantics/publishedVersionSubject
Abstract
We propose an approach to estimate the power consumption of algorithms, as a function of the frequency and number of cores, using only a very reduced set of real power measures. In addition, we also provide the ... [+]
We propose an approach to estimate the power consumption of algorithms, as a function of the frequency and number of cores, using only a very reduced set of real power measures. In addition, we also provide the formulation of a method to select the voltage–frequency scaling–concurrency throttling configurations that should be tested in order to obtain accurate estimations of the power dissipation. The power models and selection methodology are verified using two real scientific application: the stencil-based 3D MPDATA algorithm and the conjugate gradient (CG) method for sparse linear systems. MPDATA is a crucial component of the EULAG model, which is widely used in weather forecast simulations. The CG algorithm is the keystone for iterative solution of sparse symmetric positive definite linear systems via Krylov subspace methods. The reliability of the method is confirmed for a variety of ARM and Intel architectures, where the estimated results correspond to the real measured values with the average error being slightly below 5% in all cases. [-]
Investigation project
National Science Centre, Poland (UMO-2015/17/D/ST6/04059) ; MINECO and FEDER, Spain (CICYT Project TIN2014-53495-R ) ; EU (COST Program Action IC1305)Rights
© 2017 Springer International Publishing AG. Part of Springer Nature.
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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