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Title: GPU-DFC: A GPU-Based Parallel Algorithm for Computing Dynamic-Functional Connectivity of Big fMRI Data
Studying dynamic-functional connectivity (DFC) using fMRI data of the brain gives much richer information to neuroscientists than studying the brain as a static entity. Mining of dynamic connectivity graphs from these brain studies can be used to classify diseased versus healthy brains. However, constructing and mining dynamic-functional connectivity graphs of the brain can be time consuming due to size of fMRI data. In this paper, we propose a highly scalable GPU-based parallel algorithm called GPU-DFC for computing dynamic-functional connectivity of fMRI data both at region and voxel level. Our algorithm exploits sparsification of correlation matrix and stores them in CSR format. Further reduction in the correlation matrix is achieved by parallel decomposition techniques. Our GPU-DFC algorithm achieves 2 times speed-up for computing dynamic correlations compared to state-of-the-art GPU-based techniques and more than 40 times compared to a sequential CPU version. In terms of storage, our proposed matrix decomposition technique reduces the size of correlation matrices more than 100 times. Reconstructed values from decomposed matrices show comparable results as compared to the correlations with original data. The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/GPU-DFC).  more » « less
Award ID(s):
1925960
NSF-PAR ID:
10140317
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService)
Page Range / eLocation ID:
114 to 121
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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