Abstract
The current advances in hardware led to the development of the GPGPU (General-purpose computing on graphics processing units) paradigm. Thus, nowadays, the GPU (Graphics Processing Unit) is used not only for graphics programming, but also for general purpose algorithms. This paper discusses several methods regarding the use of CUDA (Compute Unified Device Architecture) for 2D and 3D image processing techniques. Some general rules for writing parallel algorithms in computer vision are pointed out. A theoretic comparison between the complexity for CPU (Central Processing Unit) and GPU implementations of image processing algorithms is given. Also, real computing times are provided for several algorithms in order to point out the actual performance gain of using the GPU over CPU. The factors that contribute to the difference between theoretic and real performance gain are also discussed.
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