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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/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:[資訊管理學系暨研究所] 會議論文

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