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 proceedingConference 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 languageEnglish (US)
Title of host publication2014 IEEE International Conference on Electro/Information Technology, EIT 2014
PublisherIEEE Computer Society
Pages479-484
Number of pages6
ISBN (Print)9781479947744
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE International Conference on Electro/Information Technology, EIT 2014 - Milwaukee, WI, United States
Duration: Jun 5 2014Jun 7 2014

Publication series

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

Other

Other2014 IEEE International Conference on Electro/Information Technology, EIT 2014
CountryUnited States
CityMilwaukee, WI
Period6/5/146/7/14

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'GPU-accelerated dynamic functional connectivity analysis for functional MRI data using OpenCL'. Together they form a unique fingerprint.

Cite this