Counting Triangles in Large Graphs on GPU

Adam Polak

Abstract: The clustering coefficient and the transitivity ratio are concepts often used in network analysis, which creates a need for fast practical algorithms for counting triangles in large graphs. Previous research in this area focused on sequential algorithms, MapReduce parallelization, and fast approximations. In this paper we propose a parallel triangle counting algorithm for CUDA GPU. We describe the implementation details necessary to achieve high performance and present the experimental evaluation of our approach. The algorithm achieves 15 to 35 times speedup over our CPU implementation, and is capable of finding 8.8 billion triangles in a 180 million edges graph in 12 seconds on the Nvidia GeForce GTX 980 GPU.