数据布局对GPU加速的IDW插值算法性能的影响
Performance Impact of Data Layout on the GPU-accelerated IDW Interpolation
摘要：
本文重点评估了不同数据布局对GPU加速IDW插值的性能影响。首先，我们重新设计和改进了我们以前的GPU实现，该实现是通过利用CUDA动态并行（CDP）功能来执行的。然后，我们实现了三个版本的GPU实现，即，简单版本、平铺版本和改进的CDP版本，基于五种布局，包括阵列结构（SoA）、Sturcutes阵列（AoS）、对齐Sturcutes阵列（AoaS）、对齐结构阵列的结构（SoAoS）和混合布局。实验结果表明：布局AoS和AoaS对于朴素版本和平铺版本都比布局SoA获得更好的性能，而布局SoA是改进CDP版本的最佳选择。我们还注意到：对于两种组合数据布局（SoAoS和Hybrid），当与其它三种基本布局相比时，没有显著的性能增益。我们建议：在实际应用中，AoaS布局是最好的选择，因为平铺版本是GPU实现的三个版本中最快的一个，特别是在单精度的上。
英文摘要：
This paper focuses on evaluating the performance impact of different data layouts on the GPU-accelerated IDW interpolation. First, we redesign and improve our previous GPU implementation that was performed by exploiting the feature CUDA Dynamic Parallel (CDP). And then, we implement three versions of GPU implementations, i.e., the naive version, the tiled version, and the improved CDP version, based on five layouts including the Structure of Arrays (SoA), the Array of Sturcutes (AoS), the Array of aligned Sturcutes (AoaS), the Structure of Arrays of aligned Structures (SoAoS), and the Hybrid layout. Experimental results show that: the layouts AoS and AoaS achieve better performance than the layout SoA for both the naive version and tiled version, while the layout SoA is the best choice for the improved CDP version. We also observe that: for the two combined data layouts (the SoAoS and the Hybrid), there are no notable performance gains when compared to other three basic layouts. We recommend that: in practical applications, the layout AoaS is the best choice since the tiled version is the fastest one among the three versions of GPU implementations, especially on single precision.
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