Making Human Connectome Faster: GPU Acceleration of Brain Network Analysis
Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China
IEEE 16th International Conference on Parallel and Distributed Systems (ICPADS), 2010
@conference{di2010making,
title={Making Human Connectome Faster: GPU Acceleration of Brain Network Analysis},
author={Di Wu, T.W. and Shan, Y. and Wang, Y. and He, Y. and Xu, N. and Yang, H.},
booktitle={2010 IEEE 16th International Conference on Parallel and Distributed Systems},
pages={593–600},
issn={1521-9097},
year={2010},
organization={IEEE}
}
The research on complex Brain Networks plays a vital role in understanding the connectivity patterns of the human brain and disease-related alterations. Recent studies have suggested a noninvasive way to model and analyze human brain networks by using multi-modal imaging and graph theoretical approaches. Both the construction and analysis of the Brain Networks require tremendous computation. As a result, most current studies of the Brain Networks are focused on a coarse scale based on Brain Regions. Networks on this scale usually consist around 100 nodes. The more accurate and meticulous voxel-base Brain Networks, on the other hand, may consist 20K to 100K nodes. In response to the difficulties of analyzing large-scale networks, we propose an acceleration framework for voxel-base Brain Network Analysis based on Graphics Processing Unit (GPU). Our GPU implementations of Brain Network construction and modularity achieve 24x and 80x speedup respectively, compared with single-core CPU. Our work makes the processing time affordable to analyze multiple large-scale Brain Networks.
April 26, 2011 by hgpu
