Home > Publications database > FAS-GED: GPU-Accelerated Graph Edit Distance Computation > print

001     1033975
005     20250822121412.0
024 7 _ |a 10.34734/FZJ-2024-06811
|2 datacite_doi
037 _ _ |a FZJ-2024-06811
041 _ _ |a English
100 1 _ |a Dabah, Adel
|0 P:(DE-Juel1)198696
|b 0
|e Corresponding author
|u fzj
111 2 _ |a The International Conference for High Performance Computing, Networking, Storage, and Analysis
|g SC24
|c Atlanta, GA
|d 2024-11-17 - 2024-11-22
|w USA
245 _ _ |a FAS-GED: GPU-Accelerated Graph Edit Distance Computation
260 _ _ |c 2024
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a CONFERENCE_POSTER
|2 ORCID
336 7 _ |a Output Types/Conference Poster
|2 DataCite
336 7 _ |a Poster
|b poster
|m poster
|0 PUB:(DE-HGF)24
|s 1736324675_5685
|2 PUB:(DE-HGF)
|x After Call
520 _ _ |a 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.
536 _ _ |a 899 - ohne Topic (POF4-899)
|0 G:(DE-HGF)POF4-899
|c POF4-899
|f POF IV
|x 0
536 _ _ |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5112
|c POF4-511
|f POF IV
|x 1
536 _ _ |a 5122 - Future Computing & Big Data Systems (POF4-512)
|0 G:(DE-HGF)POF4-5122
|c POF4-512
|f POF IV
|x 2
536 _ _ |a ATML-X-DEV - ATML Accelerating Devices (ATML-X-DEV)
|0 G:(DE-Juel-1)ATML-X-DEV
|c ATML-X-DEV
|x 3
700 1 _ |a Herten, Andreas
|0 P:(DE-Juel1)145478
|b 1
|u fzj
856 4 _ |u https://juser.fz-juelich.de/record/1033975/files/GED_poster.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1033975
|p openaire
|p open_access
|p VDB
|p driver
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)198696
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)145478
913 1 _ |a DE-HGF
|b Programmungebundene Forschung
|l ohne Programm
|1 G:(DE-HGF)POF4-890
|0 G:(DE-HGF)POF4-899
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-800
|4 G:(DE-HGF)POF
|v ohne Topic
|x 0
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Enabling Computational- & Data-Intensive Science and Engineering
|9 G:(DE-HGF)POF4-5112
|x 1
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-512
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Supercomputing & Big Data Infrastructures
|9 G:(DE-HGF)POF4-5122
|x 2
914 1 _ |y 2024
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 1 _ |a FullTexts
980 _ _ |a poster
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)JSC-20090406

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21