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28 February 2017 Accelerating hyper-spectral data processing on the multi-CPU and multi-GPU heterogeneous computing platform
Lei Zhang, Jiao Bo Gao, Yu Hu, Ying Hui Wang, Ke Feng Sun, Juan Cheng, Dan Dan Sun, Yu Li
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Proceedings Volume 10256, Second International Conference on Photonics and Optical Engineering; 102562T (2017) https://doi.org/10.1117/12.2257563
Event: Second International Conference on Photonics and Optical Engineering, 2016, Xi'an, China
Abstract
During the research of hyper-spectral imaging spectrometer, how to process the huge amount of image data is a difficult problem for all researchers. The amount of image data is about the order of magnitude of several hundred megabytes per second. The only way to solve this problem is parallel computing technology. With the development of multi-core CPU and GPU，parallel computing on multi-core CPU or GPU is increasingly applied in large-scale data processing. In this paper, we propose a new parallel computing solution of hyper-spectral data processing which is based on the multi-CPU and multi-GPU heterogeneous computing platform. We use OpenMP technology to control multi-core CPU, we also use CUDA to schedule the parallel computing on multi-GPU. Experimental results show that the speed of hyper-spectral data processing on the multi-CPU and multi-GPU heterogeneous computing platform is apparently faster than the traditional serial algorithm which is run on single core CPU. Our research has significant meaning for the engineering application of the windowing Fourier transform imaging spectrometer.
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Lei Zhang, Jiao Bo Gao, Yu Hu, Ying Hui Wang, Ke Feng Sun, Juan Cheng, Dan Dan Sun, and Yu Li "Accelerating hyper-spectral data processing on the multi-CPU and multi-GPU heterogeneous computing platform", Proc. SPIE 10256, Second International Conference on Photonics and Optical Engineering, 102562T (28 February 2017); https://doi.org/10.1117/12.2257563
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KEYWORDS
Data processing
Parallel computing
Image processing
Spectroscopy
Fourier transforms
Hyperspectral systems
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