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@INPROCEEDINGS{Dabah:1033975,
author = {Dabah, Adel and Herten, Andreas},
title = {{FAS}-{GED}: {GPU}-{A}ccelerated {G}raph {E}dit {D}istance
{C}omputation},
reportid = {FZJ-2024-06811},
year = {2024},
abstract = {Graph Edit Distance (GED) is a fundamental metric for
assessing graph similarity with critical applications across
various domains, including bioinformatics, classification,
and pattern recognition. However, the exponential
computational complexity of GED has hindered its adoption
for large-scale graph analysis. This poster presents
FAS-GED, a GPU framework for fast and accurate GED
computation. FAS-GED achieves significant performance gains
by optimizing memory accesses and minimizing data transfer
while maintaining high accuracy. FAS-GED shows up to a 300x
speedup over its CPU-based implementations on 48-CPU AMD
EPYC. Our approach surpasses existing methods in speed and
precision, demonstrating up to a 55x speedup over the
NetworkX library for small graphs and reaching optimal
solutions in $94\%$ of cases. FAS-GED is a step toward
unlocking the potential of GED for large-scale graph
analysis in real-world applications.},
month = {Nov},
date = {2024-11-17},
organization = {The International Conference for High
Performance Computing, Networking,
Storage, and Analysis, Atlanta, GA
(USA), 17 Nov 2024 - 22 Nov 2024},
subtyp = {After Call},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {899 - ohne Topic (POF4-899) / 5112 - Cross-Domain
Algorithms, Tools, Methods Labs (ATMLs) and Research Groups
(POF4-511) / 5122 - Future Computing $\&$ Big Data Systems
(POF4-512) / ATML-X-DEV - ATML Accelerating Devices
(ATML-X-DEV)},
pid = {G:(DE-HGF)POF4-899 / G:(DE-HGF)POF4-5112 /
G:(DE-HGF)POF4-5122 / G:(DE-Juel-1)ATML-X-DEV},
typ = {PUB:(DE-HGF)24},
doi = {10.34734/FZJ-2024-06811},
url = {https://juser.fz-juelich.de/record/1033975},
}