In the past decade, Graphics Processing Units have played an important role in the field of high-performance computing and they still advance new fields such as IoT, autonomous vehicles, and exascale computing. It is therefore important to understand how to extract performance from these processors, something that is not trivial. This survey discusses various optimization techniques found in 450 articles published in the last 14 years. We analyze the optimizations from different perspectives which shows that the various optimizations are highly interrelated, explaining the need for techniques such as auto-tuning.

Optimization Techniques for GPU Programming

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.

Optimizing Image Sharpening Algorithm on GPU

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Optimizing Image Sharpening Algorithm on GPU