Abstract: Modern GPUs generally provide specific hardware(such as texture, grating components and various on-chip cache) to accelerate the 2D image processing and displaying process. Programming model defines specific APIs to facili- fate image applications taking advantage of image-related GPU hardware, such as CUDA' s texture memory and OpenCI_'s Images Object. Taking the optimization of image blur algorithm on AMD GPU as an example, the paper made a deep insight into the using of OpenCL's image object on image applications,especially its advantage and disad- vantage compared to the more general optimization method based on global memory and the on-chip local memory. The experimental results demonstrate that the image object can provide better performance only when the processing image is four-channel and the amount of data to be cached is small. For other cases, optimizing with global memory and local memory can get better performance. After optimization,the speedup reaches 200x to 1000x in comparison with the well optimized CPU code,and the speedup over NV)DIA NPP version is upto 1. 3x to 5x.

Key words: AMD GPU, Blur, OpcnCI，Images object