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KARAS, P. SVOBODA, D. ZEMČÍK, P.
Original Title
GPU Optimization of Convolution for Large 3-D Real Images
Type
article in a collection out of WoS and Scopus
Language
English
Original Abstract
In this paper, we propose a method for computing convolution of large 3-D images with respect to real signals. The convolution is performed in a frequency domain using a convolution theorem. Due to properties of real signals, the algorithm can be optimized so that both time and the memory consumption are halved when compared to complex signals of the same size. Convolution is decomposed in a frequency domain using the decimation in frequency (DIF) algorithm. The algorithm is accelerated on a graphics hardware by means of the CUDA parallel computing model, achieving up to 10x speedup with a single GPU over an optimized implementation on a quad-core CPU.
Keywords
gpu, convolution, 3-D, image processing
Authors
KARAS, P.; SVOBODA, D.; ZEMČÍK, P.
RIV year
2012
Released
4. 9. 2012
Publisher
Springer Verlag
Location
Heidelberg
ISBN
978-3-642-33139-8
Book
Proceedings of ACVIS 2012
Pages from
59
Pages to
71
Pages count
13
BibTex
@inproceedings{BUT97536, author="Pavel {Karas} and David {Svoboda} and Pavel {Zemčík}", title="GPU Optimization of Convolution for Large 3-D Real Images", booktitle="Proceedings of ACVIS 2012", year="2012", pages="59--71", publisher="Springer Verlag", address="Heidelberg", doi="10.1007/978-3-642-33140-4\{_}6", isbn="978-3-642-33139-8" }