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Accelerating Wavelet Lifting on Graphics Hardware Using CUDA

机译:使用CUDA加速图形硬件上的小波提升

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The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. We show that this transform, by means of the lifting scheme, can be performed in a memory and computation-efficient way on modern, programmable GPUs, which can be regarded as massively parallel coprocessors through NVidia's CUDA compute paradigm. The three main hardware architectures for the 2D DWT (row-column, line-based, block-based) are shown to be unsuitable for a CUDA implementation. Our CUDA-specific design can be regarded as a hybrid method between the row-column and block-based methods. We achieve considerable speedups compared to an optimized CPU implementation and earlier non-CUDA-based GPU DWT methods, both for 2D images and 3D volume data. Additionally, memory usage can be reduced significantly compared to previous GPU DWT methods. The method is scalable and the fastest GPU implementation among the methods considered. A performance analysis shows that the results of our CUDA-specific design are in close agreement with our theoretical complexity analysis.
机译:离散小波变换(DWT)具有从信号处理到视频和图像压缩的广泛应用。我们表明,借助提升方案,可以在现代可编程GPU上以内存和计算有效方式执行此转换,通过NVidia的CUDA计算范例可以将其视为大规模并行协处理器。 2D DWT的三个主要硬件体系结构(行列,基于行,基于块)显示为不适合CUDA实现。我们特定于CUDA的设计可以看作是行列方法和基于块的方法之间的混合方法。与针对2D图像和3D体数据的优化CPU实现和较早的基于非CUDA的GPU DWT方法相比,我们实现了可观的加速。此外,与以前的GPU DWT方法相比,可以显着减少内存使用。该方法具有可扩展性,是所考虑方法中最快的GPU实现。性能分析表明,我们针对CUDA的设计结果与我们的理论复杂性分析非常吻合。

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