Trust: Triangle Counting Reloaded on GPUs

Triangle counting is a building block for a wide range of graph applications. Traditional wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric triangle counting beats vertex-centric design, and iii) communication-free and workload balanced graph partitioning is a grand challenge for triangle counting. On the contrary, we advocate that i) hashing can help the key operations for scalable triangle counting on Graphics Processing Units (GPUs), i.e., list intersection and graph partitioning, ii) vertex-centric design reduces both hash table construction cost and memory consumption, which is limited on GPUs. In addition, iii) we exploit graph and workload collaborative, and hashing-based 2D partitioning to scale vertex-centric triangle counting over 1000 GPUs with sustained scalability. In this article, we present Trust which performs triangle counting with the hash operation and vertex-centric mechanism at the core. To the best of our knowledge, Trust is the first work that achieves over one trillion Traversed Edges Per Second (TEPS) rate for triangle counting.