High-Performance Triangle Counting on GPUs

Counting triangles in a network is a primary step toward making sense of social networks. For instance, a graph with a large number of triangles is regarded as a “tightly knit community” with high degree of trust, because in this case all the friends (neighbors) of one vertex are also friends (connected) to each other. This work focuses on using Graphics Processing Units (GPUs) to accelerate triangle counting. To accommodate large graphs, the stat-of-the-art GPU-based triangle counting project-TriCore-simply stores the entire graph in the secondary storage to achieve communication free multi-GPU triangle counting. Our key observation is that each modern GPU server (Table I) often installs multiple GPUs which can easily overwhelm the disk bandwidth. Therefore, this paper introduces a new design for workload balancing to partition the graph and the workload in order to buffer each partitioned data in the CPU memory for faster data provisioning. Taken together, this work is the first, to the best of our knowledge, to advance the rate of triangle counting beyond 109 traversed edges per second (TEPS), as well as the first project that achieves > 108TEPS for graphs with more than ten billion edges.