Recursive Filters (RFs) are a well-known way to approximate the Gaussian convolution and, due to their computational efficiency, are intensively used in several technical and scientific fields. The accuracy of the RFs can be improved by means of the repeated application of the filter, which gives rise to the so-called K-iterated Gaussian recursive filter. In this work we propose a parallel algorithm for the implementation of the K-iterated first-order Gaussian RF for multicore architectures. This algorithm is based on a domain decomposition with overlapping strategy. The presented implementation is tailored for multicore architectures and makes use of the Pthreads library. We will show through extensive numerical tests that our parallel implementation is very efficient for large one-dimensional signals and guarantees the same accuracy level of the sequential K-iterated first-order Gaussian RF.

An algorithm for Gaussian recursive filters in a multicore architecture

Galletti, Ardelio
;
Giunta, Giulio;Marcellino, Livia;
2017-01-01

Abstract

Recursive Filters (RFs) are a well-known way to approximate the Gaussian convolution and, due to their computational efficiency, are intensively used in several technical and scientific fields. The accuracy of the RFs can be improved by means of the repeated application of the filter, which gives rise to the so-called K-iterated Gaussian recursive filter. In this work we propose a parallel algorithm for the implementation of the K-iterated first-order Gaussian RF for multicore architectures. This algorithm is based on a domain decomposition with overlapping strategy. The presented implementation is tailored for multicore architectures and makes use of the Pthreads library. We will show through extensive numerical tests that our parallel implementation is very efficient for large one-dimensional signals and guarantees the same accuracy level of the sequential K-iterated first-order Gaussian RF.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/64816
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