GPU-based spatially divided predictive partitioned vector quantization for gifts ultraspectral data compression

機構典藏 > College of Business and Management > Graduate Institute & Department of Information Management > Proceeding > Item 987654321/62343

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/62343

Title:	GPU-based spatially divided predictive partitioned vector quantization for gifts ultraspectral data compression
Authors:	Wei, Shih-chieh;Huang, Bormin
Contributors:	淡江大學資訊管理學系
Keywords:	GIFTS sounder data;Graphic processor unit;data compression
Date:	2011-07
Issue Date:	2011-10-18 16:56:35 (UTC+8)
Publisher:	IEEE Geosicence and Remote Sensing Society
Abstract:	Predictive partitioned vector quantization (PPVQ) has been proven to be an effective lossless compression scheme for ultraspectral sounder data. In previous work, we have identified the two most time-consuming stages of PPVQ for implementation on GPU. By using 4 GPUs and a spectral division design in sharing the workload, we showed a 42x speedup on NASA's Geostationary Imaging Fourier Transform Spectrometer (GIFTS) dataset compared to its original single-threaded CPU code. In this paper, an alternative spatial division design is developed to run on 4 GPUs. The experiment on the GIFTS dataset shows that a 72x speedup can be further achieved by this new design of the GPU-based PPVQ compression scheme.
Relation:	Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International, pp.221-224
DOI:	10.1109/IGARSS.2011.6048932
Appears in Collections:	[Graduate Institute & Department of Information Management] Proceeding

Files in This Item:

File	Description	Size	Format
GPU-BASED SPATIALLY DIVIDED PREDICTIVE PARTITIONED VECTOR.pdf	全文檔	357Kb	Adobe PDF	392	View/Open
index.html		0Kb	HTML	326	View/Open