GPU accelerated maximum cardinality matching algorithms for bipartite graphs
Dept. Biomedical Informatics, The Ohio State University
arXiv:1303.1379 [cs.DC], (6 Mar 2013)
@article{2013arXiv1303.1379D,
author={Deveci}, M. and {Kaya}, K. and {Ucar}, B. and {Catalyurek}, U.~V.},
title={"{GPU accelerated maximum cardinality matching algorithms for bipartite graphs}"},
journal={ArXiv e-prints},
archivePrefix={"arXiv"},
eprint={1303.1379},
primaryClass={"cs.DC"},
keywords={Computer Science – Distributed, Parallel, and Cluster Computing},
year={2013},
month={mar},
adsurl={http://adsabs.harvard.edu/abs/2013arXiv1303.1379D},
adsnote={Provided by the SAO/NASA Astrophysics Data System}
}
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 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.
March 9, 2013 by hgpu
