High-Efficiency Triangle Counting on the GPU

Abstract

Triangle counting is an important step in calculating the network clustering confficient and transitivity, and is widely used in important role recognition, spam detection, community discovery, and biological detection. In this paper, we introduced a GPU-based load balancing triangle counting scheme (GBTCS), which contains three techniques. First, we designed an algorithm for preprocessing the graph to obtain the CSR (Compressed Sparse Row Format) representation of the graph, which not only can reduce half of the memory usage of GPU, but also distribute the computational overhead to the core of the GPU. Second, we designed a SIMD (Single Instruction Multiple Data)-based set intersection algorithm that improves the thread parallel performance on the GPU. Third, we designed a load balancing algorithm to dynamically schedule the GPU workload. Performance evaluations demonstrate that our proposed scheme is 5x to 120x faster than the serial CPU algorithm.