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Computer vision algorithms acceleration using graphic processors NVIDIA CUDA
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DOI：10.1007/s10586-020-03090-6.png)
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Abstract
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Using graphic processing units (GPUs) in parallel with central processing unit in order to accelerate algorithms and applications demanding extensive computational resources has been a new trend used for the last few years. In this paper, we propose a GPU-accelerated method to parallelize different Computer vision tasks. We will report on parallelism and acceleration in computer vision applications, we provide an overview about the CUDA NVIDIA GPU programming language used. After that we will dive on GPU Architecture and acceleration used for time consuming optimization. We introduce a high-speed computer vision algorithm using graphic processing unit by using the NVIDIA's programming framework compute unified device architecture (CUDA). We realize high and significant accelerations for our computer vision algorithms and we demonstrate that using CUDA as a GPU programming language can improve Efficiency and speedups. Especially we demonstrate the efficiency of our implementations of our computer vision algorithms by speedups obtained for all our implementations especially for some tasks and for some image sizes that come up to 8061 and 5991 and 722 acceleration times.
Keywords:
Computer vision
Integral image
Prefix sum
Features extraction
GPU
NVIDIA CUDA
Image covariance
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