Publication detail

GPU Optimization of Convolution for Large 3-D Real Images

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"
}