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Sensors 2016, 16(4), 494; doi:10.3390/s16040494

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
Author to whom correspondence should be addressed.
Academic Editor: Daniele Riccio
Received: 21 January 2016 / Revised: 23 March 2016 / Accepted: 31 March 2016 / Published: 7 April 2016
View Full-Text   |   Download PDF [1046 KB, uploaded 7 April 2016]   |  


With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate. View Full-Text
Keywords: synthetic aperture radar (SAR); advanced vector extensions (AVX); graphics processing unit (GPU); imaging algorithm; collaborative computing synthetic aperture radar (SAR); advanced vector extensions (AVX); graphics processing unit (GPU); imaging algorithm; collaborative computing

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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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Zhang, F.; Li, G.; Li, W.; Hu, W.; Hu, Y. Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing. Sensors 2016, 16, 494.

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