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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Parallel Implementation of the 2D Discrete Wavelet Transform on Graphics Processing Units: Filter Bank versus Lifting
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Parallel Implementation of the 2D Discrete Wavelet Transform on Graphics Processing Units: Filter Bank versus Lifting

机译：在图形处理单元上并行实现二维离散小波变换：滤波器组与提升

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The widespread usage of the DiscreteWaveletTransform (DWT) has motivated the development of fastDWT algorithms and their tuning on all sorts of computersystems. Several studies have compared the performanceof the most popular schemes, known as Filter Bank(FBS) and Lifting (LS), and have always concluded thatLifting is the most efficient option. However, there isno such study on streaming processors such as modernGraphic Processing Units (GPUs). Current trends havetransformed these devices into powerful stream processorswith enough flexibility to perform intensive and complexfloating-point calculations. The opportunities opened upby these platforms, as well as the growing popularityof the DWT within the computer graphics field, make anew performance comparison of great practical interest.Our study indicates that FBS outperforms LS in currentgeneration GPUs. In our experiments, the actual FBS gainsrange between 10% and 140%, depending on the problemsize and the type and length of the wavelet filter. Moreover,design trends suggest higher gains in future generationGPUs.
机译：DiscreteWaveletTransform（DWT）的广泛使用推动了fastDWT算法的开发及其在各种计算机系统上的调整。多项研究已经比较了最流行的方案（称为滤波器组（FBS）和提升（LS））的性能，并始终得出结论：提升是最有效的选择。但是，在诸如现代图形处理单元（GPU）之类的流处理器上尚无此类研究。当前的趋势已将这些设备转变为功能强大的流处理器，具有足够的灵活性来执行密集和复杂的浮点计算。这些平台所带来的机遇，以及DWT在计算机图形学领域的日益普及，使得新的性能比较具有极大的实用价值。我们的研究表明，FBS在当前的GPU中性能优于LS。在我们的实验中，实际的FBS增益范围在10％到140％之间，具体取决于问题大小以及小波滤波器的类型和长度。此外，设计趋势表明，下一代GPU将获得更高的收益。

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