  The strategy of using CUDA-compatible GPUs as a parallel computation solution
to improve the performance of programs has been more and more widely approved
during the last two years since the CUDA platform was released. Its benefit
extends from the graphic domain to many other computationally intensive
domains. Tiling, as the most general and important technique, is widely used
for optimization in CUDA programs. New models of GPUs with better compute
capabilities have, however, been released, new versions of CUDA SDKs were also
released. These updated compute capabilities must to be considered when
optimizing using the tiling technique. In this paper, we implement image
interpolation algorithms as a test case to discuss how different tiling
strategies affect the program's performance. We especially focus on how the
different models of GPUs affect the tiling's effectiveness by executing the
same program on two different models of GPUs equipped testing platforms. The
results demonstrate that an optimized tiling strategy on one GPU model is not
always a good solution when execute on other GPU models, especially when some
external conditions were changed.

Distributed, Parallel, and Cluster Computing;Performance