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UID:submissions.supercomputing.org_SC24_sess533_post231@linklings.com
SUMMARY:FAS-GED: GPU-Accelerated Graph Edit Distance Computation
DESCRIPTION:Adel Dabah and Andreas Herten (Forschungszentrum Jülich)\n\nGr
 aph Edit Distance (GED) is a fundamental metric for assessing graph simila
 rity with critical applications across various domains, including bioinfor
 matics, 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 maintaini
 ng high accuracy. FAS-GED shows up to a 300x speedup over its CPU-based im
 plementations on 48-CPU AMD EPYC. Our approach surpasses existing methods 
 in speed and precision, demonstrating up to a 55x speedup over the Network
 X 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 g
 raph analysis in real-world applications.\n\nRegistration Category: Tech P
 rogram Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ayesha Afzal (Friedr
 ich-Alexander University, Erlangen-Nuremberg; Erlangen National High Perfo
 rmance Computing Center); Sally Ellingson (University of Kentucky); and Al
 an Sussman (University of Maryland)\n\n
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