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Fast filter bank convolution for three-dimensional wavelet transform by shared memory on mobile GPU computing

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Abstract

Mobile GPU applications usually constrain by the real-time requirement. However, FLOPS of mobile GPU is limited by the size and power supply of the SoC systems. Same to desktop GPUs, the mobile GPU consists of an on-chip memory hierarchy, and proper usage of memory hierarchy accelerates mobile GPU applications such as Discrete Wavelet Transform (DWT) to satisfy the real-time requirement. In this paper, by taking advantage of GPU shared memory in Tegra K1, a mobile GPU from Nvidia, we develop Bank Conflict Free Shared Memory Parallel DWT for mobile GPU applications. Computational results show that, with the display resolution of \(640 \times 350\) (EGA), Bank Conflict Free Shared Memory Parallel DWT is significantly faster than SoC CPU-based DWT. Computational results also show that, with the display resolution of \(320\times 200\) (CGA), \(640\times 480\) (VGA), \(800\times 600\) (SVGA) and \(1024\times 768\) (XGA), Bank Conflict Free Shared Memory Parallel DWT can generally satisfy the real-time requirement.

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Acknowledgments

We thank Nvidia for Jetson TK1 development board through the Tegra K1 CUDA Vision Challenge 2014–2015.

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  1. Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100094, China

    Di Zhao

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  1. Di Zhao
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Correspondence to Di Zhao.

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Zhao, D. Fast filter bank convolution for three-dimensional wavelet transform by shared memory on mobile GPU computing. J Supercomput 71, 3440–3455 (2015). https://doi.org/10.1007/s11227-015-1443-7

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  • DOI: https://doi.org/10.1007/s11227-015-1443-7

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