Presentation
FAS-GED: GPU-Accelerated Graph Edit Distance Computation
DescriptionGraph 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.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
TimeTuesday, 19 November 202412pm - 5pm EST
LocationB302-B305
TP
XO/EX