Feluca: A Two-Stage Graph Coloring Algorithm with Color-centric Paradigm on GPU

• Published in: IEEE transactions on parallel and distributed systems Vol. 32; no. 1; p. 1
• Main Authors: Zheng, Zhigao, Shi, Xuanhua, He, Ligang, Jin, Hai, Wei, Shuo, Dai, Hulin, Peng, Xuan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Apexes; Color; Color-centric Paradigm; Computational modeling; GPGPU; Graph Coloring; Graph theory; Graphics processing units; Image color analysis; Parallel processing; Parallelism; Pipeline; Recursive methods; Switches; Synchronization; Task analysis
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2020.3014173

In this paper, we propose a two-stage high-performance graph coloring algorithm, called Feluca, aiming to address the above challenges. Feluca combines the recursion-based method with the sequential spread-based method. In the first stage, Feluca uses a recursive routine to color a majority of vertices in the graph. Then, it switches to the sequential spread method to color the remaining vertices in order to avoid the conflicts of the recursive algorithm. Moreover, the following techniques are proposed to further improve the graph coloring performance. i) A new method is proposed to eliminate the cycles in the graph; ii) a top-down scheme is developed to avoid the atomic operation originally required for color selection; and iii) a novel color-centric coloring paradigm is designed to improve the degree of parallelism for the sequential spread part. All these newly developed techniques, together with further GPU-specific optimizations such as coalesced memory access, comprise an efficient parallel graph coloring solution in Feluca. We have conducted extensive experiments on NVIDIA GPUs. The results show that Feluca can achieve 1.76 - 12.98x speedup over the state-of-the-art algorithms.