GPU accelerated maximum cardinality matching algorithms for bipartite graphs

Authors:
Mehmet Deveci;Kamer Kaya;Bora Uçar;Ümit V. Çatalyürek
Affiliations:
Dept. of Biomedical Informatics, The Ohio State University and Dept. of Computer Science & Engineering, The Ohio State University;Dept. of Biomedical Informatics, The Ohio State University;CNRS and LIP, ENS Lyon, France;Dept. of Biomedical Informatics, The Ohio State University and Dept. of Electrical & Computer Engineering, The Ohio State University
Venue:
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
Year:
2013

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Abstract

We design, implement, and evaluate GPU-based algorithms for the maximum cardinality matching problem in bipartite graphs. Such algorithms have a variety of applications in computer science, scientific computing, bioinformatics, and other areas. To the best of our knowledge, ours is the first study which focuses on the GPU implementation of the maximum cardinality matching algorithms. We compare the proposed algorithms with serial and multicore implementations from the literature on a large set of real-life problems where in majority of the cases one of our GPU-accelerated algorithms is demonstrated to be faster than both the sequential and multicore implementations.