Efficient and High-quality Sparse Graph Coloring on the GPU

TL;DR

This paper introduces a GPU-based parallel graph coloring algorithm that significantly improves speed and coloring quality over existing methods, making it suitable for large-scale sparse graphs.

Contribution

The authors propose a work-efficient GPU algorithm using speculative greedy coloring, achieving better quality and higher performance than prior GPU implementations.

Findings

Achieves 4.1x average speedup over serial implementation

Outperforms NVIDIA CUSPARSE GPU implementation by 2.2x on average

Produces higher quality coloring with fewer colors

Abstract

Graph coloring has been broadly used to discover concurrency in parallel computing. To speedup graph coloring for large-scale datasets, parallel algorithms have been proposed to leverage modern GPUs. Existing GPU implementations either have limited performance or yield unsatisfactory coloring quality (too many colors assigned). We present a work-efficient parallel graph coloring implementation on GPUs with good coloring quality. Our approach employs the speculative greedy scheme which inherently yields better quality than the method of finding maximal independent set. In order to achieve high performance on GPUs, we refine the algorithm to leverage efficient operators and alleviate conflicts. We also incorporate common optimization techniques to further improve performance. Our method is evaluated with both synthetic and real-world sparse graphs on the NVIDIA GPU. Experimental results show that our proposed implementation achieves averaged 4.1x (up to 8.9x) speedup over the serial implementation. It also outperforms the existing GPU implementation from the NVIDIA CUSPARSE library (2.2x average speedup), while yielding much better coloring quality than CUSPARSE.