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Optimization of Selected Remote Sensing Algorithms for Embedded NVIDIA Kepler GPU ArchitectureThis paper evaluates the potential of embedded Graphic Processing Units in the Nvidias Tegra K1 for onboard processing. The performance is compared to a general purpose multi-core CPU and full fledge GPU accelerator. This study uses two algorithms: Wavelet Spectral Dimension Reduction of Hyperspectral Imagery and Automated Cloud-Cover Assessment (ACCA) Algorithm. Tegra K1 achieved 51 for ACCA algorithm and 20 for the dimension reduction algorithm, as compared to the performance of the high-end 8-core server Intel Xeon CPU with 13.5 times higher power consumption.
Document ID
20170005279
Acquisition Source
Goddard Space Flight Center
Document Type
Presentation
Authors
Riha, Lubomir (Technical Univ. of Ostrava Ostrava, Czechoslovakia)
Le Moigne, Jacqueline (NASA Goddard Space Flight Center Greenbelt, MD, United States)
El-Ghazawi, Tarek (George Washington Univ. Ashburn, VA, United States)
Date Acquired
June 7, 2017
Publication Date
July 26, 2015
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN24647
Report Number: GSFC-E-DAA-TN24647
Meeting Information
Meeting: IEEE International Geoscience and Remote Sensing (IGARSS 2015) Conference
Location: Milan
Country: Italy
Start Date: July 26, 2015
End Date: July 31, 2015
Sponsors: Institute of Electrical and Electronics Engineers
Distribution Limits
Public
Copyright
Public Use Permitted.

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