GPU-Accelerated Dynamic Graph Coloring

Abstract

The graph coloring is a classic problem in the graph theory, which can be leveraged to mark two objects with a certain relationship with different colors. Existing graph coloring solutions mainly focus on efficiently calculating high-quality coloring of static graphs. However, many graphs in the real world are highly dynamic and the coloring result changes when the graph is updated. Repeated adoption of static graph coloring schemes will incur prohibitive costs. Although some CPU-based incremental graph coloring methods have been proposed recently, they become inefficient when facing dense graphs and large batch updates. In this paper, we explore the dynamic graph coloring solution by utilizing the powerful parallel processing capabilities of GPU and propose a CPU-GPU heterogeneous method. We conduct extensive experiments comparing our algorithm with the existing methods. The results confirm that our algorithm is superior to others in many aspects such as coloring efficiency.