Evaluating application performance and energy consumption on hybrid CPU+GPU architecture

Padoin, Edson Luiz; Pilla, Laércio Lima; Boito, Francieli Zanon; Kassick, Rodrigo; Velho, Pedro Isaacsson; Navaux, Philippe O. A.

doi:10.1007/s10586-012-0219-6

Cited by 21 publications

(10 citation statements)

References 22 publications

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“…Padoin et al [25] investigate performance and power consumption of an agroforestry application and show that changing workload can drastically improve energy efficiency of CPU + GPU heterogeneous architectures.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the performance and energy efficiency of the COSMO-ART model system

et al. 2014

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Registro de acceso restringido Este recurso no está disponible en acceso abierto por política de la editorial. No obstante, se puede acceder al texto completo desde la Universitat Jaume I o si el usuario cuenta con suscripción. Registre d'accés restringit Aquest recurs no està disponible en accés obert per política de l'editorial. No obstant això, es pot accedir al text complet des de la Universitat Jaume I o si l'usuari compta amb subscripció. Restricted access item This item isn't open access because of publisher's policy. The full--text version is only available from Jaume I University or if the user has a running suscription to the publisher's contents.

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Section: Related Workmentioning

confidence: 99%

Evaluating the performance and energy efficiency of the COSMO-ART model system

et al. 2014

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“…GPUs are made aiming at massively parallel processing, to achieve this they use hundreds of processing units working together. These characteristics lead to its superior energy efficiency if compared with CPUs systems [14].…”

Section: Related Workmentioning

confidence: 99%

Improving Performance and Energy Efficiency of Geophysics Applications on GPU Architectures

Pavan

Serpa

Carreno

et al. 2019

Communications in Computer and Information Science

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Energy and performance of parallel systems are an increasing concern for new large-scale systems. Research has been developed in response to this challenge aiming the manufacture of more energy efficient systems. In this context, this paper proposes optimization methods to accelerate performance and increase energy efficiency of geophysics applications used in conjunction to algorithm and GPU memory characteristics. The optimizations we developed applied to Graphics Processing Units (GPU) algorithms for stencil applications achieve a performance improvement of up to 44.65% compared with the read-only version. The computational results have shown that the combination of use read-only memory, the Z-axis internalization and reuse of specific architecture registers allow increase the energy efficiency of up to 54.11% when shared memory was used and increase of up to 44.53% when read-only was used.

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“…GPUs are made aiming massively parallel processing and to achieve this they use hundreds of processing units working together. These characteristics lead to its superior energy efficiency when compared with CPUs [Padoin et al 2013b].…”

Section: Related Workmentioning

confidence: 99%

Strategies to Improve the Performance and Energy Efficiency of Stencil Computations for NVIDIA GPUs

Pavan¹,

Serpa²,

Martínez³

et al. 2018

Anais Do Workshop Em Desempenho De Sistemas Computacionais E De Comunicação (WPerformance)

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Energy and performance of parallel systems are an increasing concern for new large-scale systems. Research has been developed in response to this challenge aiming the manufacture of more energy efficient systems. In this context, we improved the performance and achieved energy efficiency by the development of three different strategies which use the GPU memory subsystem (global-, shared-, and read-only- memory). We also develop two optimizations to use data locality and use of registers of GPU architecture. Our developed optimizations were applied to GPU algorithms for stencil applications achieve a performance improvement of up to 201:5% in K80 and 264:6% in P 100 when used shared memory and read-only cache respectively over the naive version. The computational results have shown that the combination of use read-only memory, the Z-axis internalization of stencil application and reuse of specific architecture registers allow increasing the energy efficiency of up to 255:6% in K80 and 314:8% in P 100.

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Evaluating application performance and energy consumption on hybrid CPU+GPU architecture

Cited by 21 publications

References 22 publications

Evaluating the performance and energy efficiency of the COSMO-ART model system

Evaluating the performance and energy efficiency of the COSMO-ART model system

Improving Performance and Energy Efficiency of Geophysics Applications on GPU Architectures

Strategies to Improve the Performance and Energy Efficiency of Stencil Computations for NVIDIA GPUs

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