TY  - THES
AU  - Kirchner, Tabea
TI  - Parallelization and optimization of measures in Network Neuroscience
PB  - FH Aachen Campus Jülich
VL  - Masterarbeit
M1  - FZJ-2022-04854
SP  - 107 p.
PY  - 2022
N1  - Masterarbeit, FH Aachen Campus Jülich, 2022
AB  - The 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.
LB  - PUB:(DE-HGF)19
UR  - https://juser.fz-juelich.de/record/911595
ER  -