TY  - CONF
AU  - Dabah, Adel
AU  - Herten, Andreas
TI  - FAS-GED: GPU-Accelerated Graph Edit Distance Computation
M1  - FZJ-2024-06811
PY  - 2024
AB  - 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.
T2  - The International Conference for High Performance Computing, Networking, Storage, and Analysis
CY  - 17 Nov 2024 - 22 Nov 2024, Atlanta, GA (USA)
Y2  - 17 Nov 2024 - 22 Nov 2024
M2  - Atlanta, GA, USA
LB  - PUB:(DE-HGF)24
DO  - DOI:10.34734/FZJ-2024-06811
UR  - https://juser.fz-juelich.de/record/1033975
ER  -