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Making Human Connectome Faster: GPU Acceleration of Brain Network Analysis

Di Wu, Tianji Wu, Yi Shan, Yu Wang, Yong He, Ningyi Xu, Huazhong Yang
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}

}

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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.
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