Abstract #4480
A GPU-based parallel computing framework for accelerating graph theoretical analyses
Tsang-Chu Yu 1 , Yi-Ping Chao 1 , Li-Wei Kuo 2 , Chung-Chih Lin 1 , Shih-Yen Lin 2,3 , Hengtai Jan 2 , Claudia Metzler-Baddeley 4 , and Derek Jones 4
1
Department of Computer Science and
Information Engineering, Chang Gung University, Taoyuan,
Taiwan,
2
Institute
of Biomedical Engineering and Nanomedicine, National
Health Research Institutes, Miaoli, Taiwan,
3
Department
of Computer Science, National Chiao Tung University,
Hsinchu, Taiwan,
4
School of Psychology,
Cardiff University, Cardiff, United Kingdom
The main purpose of this study is to develop a graphics
processing unit based framework for brain network
analysis that permit networks comprising much larger
numbers of nodes and provide the acceleration for
processing. From the results, our implementation for the
calculation of all pair shortest paths could reduce half
of time with brain connectivity toolbox (BCT) and 638x
speedup with Gretna in simulation random network with
larger number of nodes (>8k). Moreover, our algorithm
also shows better performance in human brain data with
1.37x and 21x speedup in comparison with BCT and Gretna
respectively.
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