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Open AccessArticle

GPU-Based Lossless Compression of Aurora Spectral Data using Online DPCM

1
School of Electronic Engineering, Xidian University, Xi’an 710071, China
2
Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1635; https://doi.org/10.3390/rs11141635
Received: 25 May 2019 / Revised: 2 July 2019 / Accepted: 7 July 2019 / Published: 10 July 2019
(This article belongs to the Special Issue GPU Computing for Geoscience and Remote Sensing)
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Abstract

It is well known that aurorae have very high research value, but the data volume of aurora spectral data is very large, which brings great challenges to storage and transmission. To alleviate this problem, compression of aurora spectral data is indispensable. This paper presents a parallel Compute Unified Device Architecture (CUDA) implementation of the prediction-based online Differential Pulse Code Modulation (DPCM) method for the lossless compression of the aurora spectral data. Two improvements are proposed to improve the compression performance of the online DPCM method. One is on the computing of the prediction coefficients, and the other is on the encoding of the residual. In the CUDA implementation, we proposed a decomposition method for the matrix multiplication to avoid redundant data accesses and calculations. In addition, the CUDA implementation is optimized with a multi-stream technique and multi-graphics processing unit (GPU) technique, respectively. Finally, the average compression time of an aurora spectral image reaches about 0.06 s, which is much less than the 15 s aurora spectral data acquisition time interval and can save a lot of time for transmission and other subsequent tasks. View Full-Text
Keywords: aurora spectral data; online DPCM; CUDA; shared memory and registers; multi-stream technique; multi-GPU technique aurora spectral data; online DPCM; CUDA; shared memory and registers; multi-stream technique; multi-GPU technique
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Li, J.; Wu, J.; Jeon, G. GPU-Based Lossless Compression of Aurora Spectral Data using Online DPCM. Remote Sens. 2019, 11, 1635.

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