Abstract
With the popularity of GPU which has the high performance computing feature, more and more algorithms have been successfully transplanted to the GPU platform and achieved high efficiency. But existing videos or images processing methods, such as demosaicing algorithm, have not fully exploited the parallel computing capacity of heterogeneous processing platform and the video frame rates can’t meet real-time requirements. In order to take full advantage of the computing power of GPU under the heterogeneous processing platform, an optimization scheme is proposed in this paper. We use the demosiacing algorithm as a case and modify the algorithm. By exploiting the GPU’s memory hierarchy, the optimization scheme improves the parallelism of the algorithm while reducing the memory access latency, and greatly reduces the execution time. Then we achieve the zero-copy at the same time. The experimental results show that optimization version has a significant performance improvement, the optimized OpenCL version is up to 6x comparing with the basic OpenCL version about kernel execution.
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Acknowledgements
The work is supported by the Science and Technology Key Project of Tianjin under Grant No. 17YFZCGX01180 and Tianjin Key Laboratory of Advanced Networking (TANK).
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Wang, T., Guo, W., Wei, J. (2019). An Optimization Scheme for Demosaicing Algorithm on GPU Using OpenCL. In: Xu, W., Xiao, L., Li, J., Zhu, Z. (eds) Computer Engineering and Technology. NCCET 2018. Communications in Computer and Information Science, vol 994. Springer, Singapore. https://doi.org/10.1007/978-981-13-5919-4_14
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DOI: https://doi.org/10.1007/978-981-13-5919-4_14
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