Optimizing Image Sharpening Algorithm on GPU

TL;DR: This paper proposes a complete solution to implement and optimize sharpness on GPU and includes five major and effective techniques: Data Transfer Optimization, Kernel Fusion, Vectorization for Data Locality, Border and Reduction Optimization.

Abstract: Sharpness is an algorithm used to sharpen images. As the increase of image size, resolution, and the requirements for real-time processing, the performance of sharpness needs to get improved greatly. The independent pixel calculation of sharpness makes a good opportunity to use GPU to largely accelerate the performance. However, to transplant it to GPU, one challenge is that sharpness involves several stages to execute. Each stage has its own characteristics, either with or without data dependency to other stages. Based on those characteristics, this paper proposes a complete solution to implement and optimize sharpness on GPU. Our solution includes five major and effective techniques: Data Transfer Optimization, Kernel Fusion, Vectorization for Data Locality, Border and Reduction Optimization. Experiments show that, compared to a well-optimized CPU version, our GPU solution can reach 10.7a#x007E; 69.3 times speedup for different image sizes on an AMD Fire Pro W8000 GPU.