Tiling for Performance Tuning on Different Models of GPUs
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 programs performance. We especially focus on how the different models of GPUs affect the tilings 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.
parallel GPU CUDA tiling performance
Chang Xu Steven R. Kirk Samantha Jenkins
Department of Information Engineering Zhejiang Business Technology Institute Ningbo, China Department of Economics and IT University West Trollhattan, Sweden
国际会议
Second International Symposium on Information Science and Engineering(第二届信息科学与工程国际会议)
上海
英文
500-504
2009-12-26（万方平台首次上网日期，不代表论文的发表时间）