GPU accelerated maximum cardinality matching algorithms for bipartite graphs

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.
Liste complète des métadonnées

Cited literature [18 references]  Display  Hide  Download

https://hal.inria.fr/hal-00923449
Contributor : Equipe Roma <>
Submitted on : Thursday, January 2, 2014 - 7:46:51 PM
Last modification on : Friday, April 20, 2018 - 3:44:26 PM
Document(s) archivé(s) le : Saturday, April 8, 2017 - 10:35:51 AM

File

matchingGPU.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Mehmet Deveci, Kamer Kaya, Bora Uçar, Umit Catalyurek. GPU accelerated maximum cardinality matching algorithms for bipartite graphs. Euro-Par 2013, Aug 2013, Aachen, Germany. pp.850-861, ⟨10.1007/978-3-642-40047-6_84⟩. ⟨hal-00923449⟩

Share

Metrics

Record views

422

Files downloads

507