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001033975 005__ 20250822121412.0
001033975 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-06811
001033975 037__ $$aFZJ-2024-06811
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001033975 1001_ $$0P:(DE-Juel1)198696$$aDabah, Adel$$b0$$eCorresponding author$$ufzj
001033975 1112_ $$aThe International Conference for High Performance Computing, Networking, Storage, and Analysis$$cAtlanta, GA$$d2024-11-17 - 2024-11-22$$gSC24$$wUSA
001033975 245__ $$aFAS-GED: GPU-Accelerated Graph Edit Distance Computation
001033975 260__ $$c2024
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001033975 520__ $$aGraph 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.
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001033975 7001_ $$0P:(DE-Juel1)145478$$aHerten, Andreas$$b1$$ufzj
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