Abstract
Gaussian convolution operation is a fundamental procedure in several data analysis tasks and scientific fields. For example, Gaussian convolution is a central step in data assimilation and machine learning and it is also frequently used in image and signal processing. Gaussian recursive filters are a class of methods designed to approximate Gaussian convolutions in a fast way. In De Luca et al. (2019 15th international conference on signal-image technology and internet-based systems (SITIS), pp 941–648, 2019), we presented a parallel implementation of the K-iterated first-order Gaussian recursive filter. This algorithm has been proved to be very efficient and accurate. Here, we provide a new GPU-parallel implementation which is based on the third order recursive filter. This filter guarantees larger accuracy and a lower computational cost with respect to the first-order one. To manage an efficient memory strategy access, and to achieve better performance results, our algorithm exploits the CUDA capabilities available on the GPU environment. Results in terms of performance and accuracy are provided in tests and experiments.
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This article is part of the topical collection “Advances on Signal Image Technology and Internet-based Systems” guest edited by Albert Dipanda, Luigi Gallo and Kokou Yetongnon.
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De Luca, P., Galletti, A. & Marcellino, L. GPU-CUDA Implementation of the Third Order Gaussian Recursive Filter. SN COMPUT. SCI. 3, 78 (2022). https://doi.org/10.1007/s42979-021-00960-7
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DOI: https://doi.org/10.1007/s42979-021-00960-7