title: GPU accelerated dynamic functional connectivity analysis for functional MRI data.
abstract: Recent advances in multi-core processors and graphics card based computational technologies have paved the way for an improved and dynamic utilization of parallel computing techniques. Numerous applications have been implemented for the acceleration of computationally-intensive problems in various computational science fields including bioinformatics, in which big data problems are prevalent. In neuroimaging, dynamic functional connectivity (DFC) analysis is a computationally demanding method used to investigate dynamic functional interactions among different brain regions or networks identified with functional magnetic resonance imaging (fMRI) data. In this study, we implemented and analyzed a parallel DFC algorithm based on thread-based and block-based approaches. The thread-based approach was designed to parallelize DFC computations and was implemented in both Open Multi-Processing (OpenMP) and Compute Unified Device Architecture (CUDA) programming platforms. Another approach developed in this study to better utilize CUDA architecture is the block-based approach, where parallelization involves smaller parts of fMRI time-courses obtained by sliding-windows. Experimental results showed that the proposed parallel design solutions enabled by the GPUs significantly reduce the computation time for DFC analysis. Multicore implementation using OpenMP on 8-core processor provides up to 7.7× speed-up. GPU implementation using CUDA yielded substantial accelerations ranging from 18.5× to 157× speed-up once thread-based and block-based approaches were combined in the analysis. Proposed parallel programming solutions showed that multi-core processor and CUDA-supported GPU implementations accelerated the DFC analyses significantly. Developed algorithms make the DFC analyses more practical for multi-subject studies with more dynamic analyses.
authors: Department of Computer Engineering, Sakarya University, Sakarya, Turkey. Electronic address: dakgun@sakarya.edu.tr.
authors: Devrim
authors: D
authors: Akgün
authors: Department of Computer Science and Information Systems, Texas A&M University - Commerce, Commerce, TX, USA.
authors: Ünal
authors: Ü
authors: Sakoğlu
authors: Department of Computer Science and Information Systems, Texas A&M University - Commerce, Commerce, TX, USA.
authors: Johnny
authors: J
authors: Esquivel
authors: VA North Texas Health Care System, Dallas, TX, USA; Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA.
authors: Bryon
authors: B
authors: Adinoff
authors: Department of Computer Science and Information Systems, Texas A&M University - Commerce, Commerce, TX, USA.
authors: Mutlu
authors: M
authors: Mete
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D003196:Computer Graphics; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: CUDA; Dynamic functional connectivity; Functional magnetic resonance imaging; GPU computing; OpenMP; fMRI