GPU accelerated maximum cardinality matching algorithms for bipartite graphs

TL;DR

This paper introduces GPU-accelerated algorithms for maximum cardinality matching in bipartite graphs, demonstrating significant speedups over traditional serial and multicore methods across various real-world datasets.

Contribution

It is the first to focus on GPU implementation of these algorithms, providing novel, efficient solutions for large-scale bipartite graph matching problems.

Findings

GPU algorithms outperform serial implementations in most cases.

GPU algorithms outperform multicore implementations in most cases.

Significant speedups achieved on real-life datasets.

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 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.