HTC: Hybrid vertex-parallel and edge-parallel Triangle Counting

Abstract

Graph algorithms (e.g., triangle counting) are widely used to find the deep association of data in various real-world applications such as friend recommendation and junk mail detection. However, even if using the massive parallelism of GPU, existing methods fail to run triangle counting queries efficiently on various large graphs. In this paper, we propose a fast hybrid algorithm HTC, which can utilize both vertex-parallel and edge-parallel paradigm and deliver much better performance on GPU. Different from current GPU implementations, HTC adaptively selects different parallel paradigm for different vertices. Also, bitwise-based intersection on segmented bitmap is proposed instead of naive binary search. Furthermore, preprocessing techniques like graph reordering and recursive clipping are adopted to optimize the graph structure. Extensive experiments show that HTC outperforms all state-of-the-art triangle counting implementations on GPU by 1.2x~42x.