Parallel connected-component labeling algorithm for GPGPU applications
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Abstract
This paper proposes a new connected component labeling algorithm for GPGPU applications based on NVIDIA's CUDA. Various approaches and algorithms for connected component labeling with minimal execution time were designed, but the most of them have been focused on optimizing CPU algorithm. Therefore it is hard to apply these approaches to GPGPU programming models such as NVIDIA's CUDA. Today, GPGPU (General Purpose Graphic Processing Unit) technologies offer dedicated parallel hardware and programming model, and many applications are being moved onto the GPGPU. This algorithm is a multi-pass algorithm to utilize for GPGPU applications, and evaluation results show that maximum speedup is more than double compared with conventional CPU algorithms.
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