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@MASTERSTHESIS{Kirchner:911595,
author = {Kirchner, Tabea},
title = {{P}arallelization and optimization of measures in {N}etwork
{N}euroscience},
school = {FH Aachen Campus Jülich},
type = {Masterarbeit},
reportid = {FZJ-2022-04854},
pages = {107 p.},
year = {2022},
note = {Masterarbeit, FH Aachen Campus Jülich, 2022},
abstract = {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.},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
(SDLs) and Research Groups (POF4-511) / SLNS - SimLab
Neuroscience (Helmholtz-SLNS)},
pid = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)Helmholtz-SLNS},
typ = {PUB:(DE-HGF)19},
url = {https://juser.fz-juelich.de/record/911595},
}