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000911595 005__ 20221122131005.0
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000911595 1001_ $$0P:(DE-Juel1)173687$$aKirchner, Tabea$$b0$$eCorresponding author
000911595 245__ $$aParallelization and optimization of measures in Network Neuroscience$$f - 2022-08-09
000911595 260__ $$c2022
000911595 300__ $$a107 p.
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000911595 502__ $$aMasterarbeit, FH Aachen Campus Jülich, 2022$$bMasterarbeit$$cFH Aachen Campus Jülich$$d2022
000911595 520__ $$aThe main goal of neuroscience is to understand the human brain. To achieve this, the area Network Neuroscience has evolved. This scientific field describes the human brain as a network comprising of neural elements (nodes), and connection between these elements (edges). Graph analysis algorithms can therefore be applied to gain insights into the brain. The Brain Connectivity Toolbox (BCT) is a Matlab toolbox that includes graph algorithms calculating measures in Network Neuroscience. This toolbox only provides sequential versions of these algorithms, which limits the size of the input graph. To overcome this limit, we implement optimized shared and distributed memory algorithms from this toolbox. For the implementations, the Boost Graph Library is used. This library provides a generic interface for the graph data structure. Experiments that are run on the Jülich supercomputer JUWELS show that a performance improvement can be achieved by this parallelization.
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000911595 9141_ $$y2022
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