  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.
