GPU-accelerated dynamic functional connectivity analysis for functional MRI data using OpenCL

Devrim Akgun; Unal Sakoglu; Mutlu Mete; Johnny Esquivel; Bryon Adinoff

doi:10.1109/EIT.2014.6871811

GPU-accelerated dynamic functional connectivity analysis for functional MRI data using OpenCL

Devrim Akgun, Unal Sakoglu, Mutlu Mete, Johnny Esquivel, Bryon Adinoff

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Intense computations in engineering and science, especially bioinformatics have been made practical by the recent advances in Graphical Processing Unit (GPU) computing technology. In this study, implementation and performance evaluations for a GPU-accelerated dynamic functional connectivity (DFC) analysis, which is an analysis method for investigating dynamic interactions among different brain networks, is presented. Open Computing Library (OpenCL), which provides a general framework for GPU computing, is utilized, and it is shown to reduce the DFC analysis computation time. The parallel implementation with OpenCL provides up to 10x speed-up over sequential implementation.

Original language	English (US)
Title of host publication	2014 IEEE International Conference on Electro/Information Technology, EIT 2014
Publisher	IEEE Computer Society
Pages	479-484
Number of pages	6
ISBN (Print)	9781479947744
DOIs	https://doi.org/10.1109/EIT.2014.6871811
State	Published - 2014
Event	2014 IEEE International Conference on Electro/Information Technology, EIT 2014 - Milwaukee, WI, United States Duration: Jun 5 2014 → Jun 7 2014

Publication series

Name	IEEE International Conference on Electro Information Technology
ISSN (Print)	2154-0357
ISSN (Electronic)	2154-0373

Other

Other	2014 IEEE International Conference on Electro/Information Technology, EIT 2014
Country/Territory	United States
City	Milwaukee, WI
Period	6/5/14 → 6/7/14

ASJC Scopus subject areas

Computer Science Applications
Information Systems
Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/EIT.2014.6871811

Cite this

Akgun, D., Sakoglu, U., Mete, M., Esquivel, J., & Adinoff, B. (2014). GPU-accelerated dynamic functional connectivity analysis for functional MRI data using OpenCL. In 2014 IEEE International Conference on Electro/Information Technology, EIT 2014 (pp. 479-484). Article 6871811 (IEEE International Conference on Electro Information Technology). IEEE Computer Society. https://doi.org/10.1109/EIT.2014.6871811

GPU-accelerated dynamic functional connectivity analysis for functional MRI data using OpenCL. / Akgun, Devrim; Sakoglu, Unal; Mete, Mutlu et al.
2014 IEEE International Conference on Electro/Information Technology, EIT 2014. IEEE Computer Society, 2014. p. 479-484 6871811 (IEEE International Conference on Electro Information Technology).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Akgun, D, Sakoglu, U, Mete, M, Esquivel, J & Adinoff, B 2014, GPU-accelerated dynamic functional connectivity analysis for functional MRI data using OpenCL. in 2014 IEEE International Conference on Electro/Information Technology, EIT 2014., 6871811, IEEE International Conference on Electro Information Technology, IEEE Computer Society, pp. 479-484, 2014 IEEE International Conference on Electro/Information Technology, EIT 2014, Milwaukee, WI, United States, 6/5/14. https://doi.org/10.1109/EIT.2014.6871811

Akgun D, Sakoglu U, Mete M, Esquivel J, Adinoff B. GPU-accelerated dynamic functional connectivity analysis for functional MRI data using OpenCL. In 2014 IEEE International Conference on Electro/Information Technology, EIT 2014. IEEE Computer Society. 2014. p. 479-484. 6871811. (IEEE International Conference on Electro Information Technology). doi: 10.1109/EIT.2014.6871811

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GPU-accelerated dynamic functional connectivity analysis for functional MRI data using OpenCL

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