Parallelization of a video segmentation algorithm on CUDA-enabled graphics processing units

Abstract

Nowadays, Graphics Processing Units (GPU) are emerging as SIMD coprocessors for general purpose computations, specially after the launch of nVIDIA CUDA. Since then, some libraries have been implemented for matrix computation and image processing. However, in real video applications some stages need irregular data distributions and the parallelism is not so inherent. This paper presents the parallelization of a video segmentation application on GPU hardware, which implements an algorithm for abrupt and gradual transitions detection. A critical part of the algorithm requires highly intensive computation for video frames features calculation. Results on three CUDA-enabled GPUs are encouraging, because of the significant speedup achieved. They are also compared with an OpenMP version of the algorithm, running on two platforms with multiples cores. © 2009 Springer.