TriCore: Parallel Triangle Counting on GPUs

Abstract

Exact triangle counting algorithm enumerates the triangles in a graph by identifying the common neighbors of two vertices of each edge. In this work, we present TriCore, a scalable GPU-based triangle counting system that consists of three major techniques. First, we design a binary search based algorithm that can increase both the thread parallelism and memory performance on Graphics Processing Units (GPUs), both of which are absent from prior work. Second, in contrast to prior attempts which require multiple graph representations, i.e., compressed sparse row (CSR), edge list, and bitmap, to be present in the GPU memory, TriCore evenly partitions and distributes the partitioned CSR data across all the GPUs, and uses a streaming buffer to load the edge list from the CPU memory on the fly. This design enables TriCore to process the graphs that are orders of magnitude larger than the GPU memory. Third, we further develop a dynamic workload management technique to balance the workload across GPUs. our evaluation demonstrates that TriCore on a single GPU can count the triangles in the billion-edge Twitter graph within 24 seconds, that is, 22× faster than the state-of-the-art CPU project which uses CPUs that are 8× more expensive. When processing big graphs (up to 33.4 billion edges) that are ∼22× larger than the memory size of a single GPU, it achieves 24× speedup when scaling from 1 to 32 GPUs.