Optimization of selected remote sensing algorithms for embedded Nvidia Kepler GPU architecture

This paper evaluates the potential of embedded Graphic Processing Units in the Nvidia's 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.