Tiling for Performance Tuning on Different Models of GPUs

TL;DR

This paper investigates how different GPU models impact the effectiveness of tiling strategies in CUDA programs, using image interpolation as a test case, highlighting the need for model-specific optimization.

Contribution

It analyzes the influence of various GPU models on tiling performance, emphasizing that optimization strategies must be tailored to specific hardware for best results.

Findings

Tiling strategies vary in effectiveness across GPU models.

Optimizations on one GPU model may not transfer well to others.

External conditions influence tiling performance significantly.

Abstract

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.