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    黄 为 魏迎梅 宋汉辰 吴玲达. 基于并行小波算法的DEM数据多分辨率模型构建[J]. 计算机研究与发展, 2010, 47(6): 1026-1031.
    引用本文: 黄 为 魏迎梅 宋汉辰 吴玲达. 基于并行小波算法的DEM数据多分辨率模型构建[J]. 计算机研究与发展, 2010, 47(6): 1026-1031.
    Huang Wei, Wei Yingmei, Song Hanchen, and Wu Lingda. A Parallel Algorithm for Multi-Resolution Representation of DEM Based on Discrete Wavelet Analysis[J]. Journal of Computer Research and Development, 2010, 47(6): 1026-1031.
    Citation: Huang Wei, Wei Yingmei, Song Hanchen, and Wu Lingda. A Parallel Algorithm for Multi-Resolution Representation of DEM Based on Discrete Wavelet Analysis[J]. Journal of Computer Research and Development, 2010, 47(6): 1026-1031.

    基于并行小波算法的DEM数据多分辨率模型构建

    A Parallel Algorithm for Multi-Resolution Representation of DEM Based on Discrete Wavelet Analysis

    • 摘要: 由于离散小波的多分辨率分析特性和DEM数据多分辨率模型的一致性,可以采用离散小波算法来构建DEM多分辨率模型,同时针对现有的基于小波的DEM数据多分辨率模型构建中运算量过大的问题,采用了基于并行的小波算法来进行多分辨率模型构建.由于DEM数据等价于二维的灰度图像,可以作为二维信号来进行处理.首先给出了二维离散信号小波变换的Mallat算法,进行了算法的可并行性分析;然后描述了各处理机中的局部数组的数据结构并讨论了并行算法中多处理机逻辑拓扑间的数据传递.实验结果证明,小波算法适宜于并行,且将其应用于DEM数据多分辨率模型构建时能获得理想的加速比,基于并行的小波算法能极大地加速多分辨率模型的构建速度.

       

      Abstract: Because of the discrete wavelets characteristics of multi-resolution, discrete wavelet transformation can be used to construct multi-resolution of the DEM data. Nevertheless, the intensive computation of DWT has become a significant bottleneck in real-time applications when the size of DEM data is large enough. It presents a parallel processing framework to overcome the drawbacks of this problem and achieves a fast parallel building of the DEM datas multi-resolution model. As the DEM data is equivalent to two-dimensional gray-scale images, it can be processed as two-dimensional signal. This paper first gives the Mallat algorithm for a two-dimensional discrete wavelet transform (DWT). Based on this algorithm, the parallel property of the DWT is analyzed. Result shows that the Mallat algorithm is easy to carry out in parallel mode because of the local nature of the DWT. Then analyzed are the data communications between the logical topology of the multi-processors. Concretely, the authors present a data structure of the local array declared on the processor and describe the communication relationships between the various processors on two-dimensional grids. Simulation tests show that the parallel model leads to a satisfactory scalability of the algorithm and high speedups are achieved.

       

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