Optimization of Selected Remote Sensing Algorithms for Many-Core Architectures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F16%3A86099011" target="_blank" >RIV/61989100:27740/16:86099011 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7471409" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7471409</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/JSTARS.2016.2558492" target="_blank" >10.1109/JSTARS.2016.2558492</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimization of Selected Remote Sensing Algorithms for Many-Core Architectures

Popis výsledku v původním jazyce

This paper evaluates the potential of embedded graphic processing units (GPU) in the Nvidia's Tegra K1 for onboard processing. The performance is compared to a general purpose multicore central processing unit (CPU), a full-fledge GPU accelerator, and an Intel Xeon Phi coprocessor, for two representative potential applications, wavelet spectral dimension reduction of hyperspectral imagery and automated cloud-cover assessment (ACCA). For these applications, Tegra K1 achieved 51% performance for the ACCA algorithm and 20% performance for the dimension reduction algorithm, as compared to the performance of the high-end eight-core server Intel Xeon CPU which has a 13.5 times higher power consumption. This paper also shows the potential of modern high-performance computing accelerators for algorithms such as the ones for which the paper presents an optimized parallel implementation. The two algorithms that were tested mostly contain spatially localized computations, and one can assume that all image processing algorithms containing localized computations would exhibit similar speed-ups when implemented on these parallel architectures.

Název v anglickém jazyce

Optimization of Selected Remote Sensing Algorithms for Many-Core Architectures

Popis výsledku anglicky

This paper evaluates the potential of embedded graphic processing units (GPU) in the Nvidia's Tegra K1 for onboard processing. The performance is compared to a general purpose multicore central processing unit (CPU), a full-fledge GPU accelerator, and an Intel Xeon Phi coprocessor, for two representative potential applications, wavelet spectral dimension reduction of hyperspectral imagery and automated cloud-cover assessment (ACCA). For these applications, Tegra K1 achieved 51% performance for the ACCA algorithm and 20% performance for the dimension reduction algorithm, as compared to the performance of the high-end eight-core server Intel Xeon CPU which has a 13.5 times higher power consumption. This paper also shows the potential of modern high-performance computing accelerators for algorithms such as the ones for which the paper presents an optimized parallel implementation. The two algorithms that were tested mostly contain spatially localized computations, and one can assume that all image processing algorithms containing localized computations would exhibit similar speed-ups when implemented on these parallel architectures.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Journal of selected topics in applied earth observations and remote sensing

ISSN

1939-1404

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

5576-5587

Kód UT WoS článku

000391468900003

EID výsledku v databázi Scopus

2-s2.0-84969555915