High Performance Detection of Strongly Connected Components in Sparse Graphs on GPUs

Abstract

Detecting strongly connected components (SCC) has been broadly used in many real-world applications. To speedup SCC detection for large-scale graphs, parallel algorithms have been proposed to leverage modern GPUs. Existing GPU implementations are able to get speedup on synthetic graph instances, but show limited performance when applied to large-scale real-world datasets. In this paper, we present a parallel SCC detection implementation on GPUs that achieves high performance on both synthetic and real-world graphs. We use a hybrid method that divides the algorithm into two phases. Our method is able to dynamically change parallelism strategies to maximize performance for each algorithm phase. We then orchestrates the graph traversal kernel with customized strategy for each phase, and employ algorithm extensions to handle the serialization problem caused by irregular graph properties. Our design is carefully implemented to take advantage of the GPU hardware. Evaluation with diverse graphs on the NVIDIA K20c GPU shows that our proposed implementation achieves an average speedup of 5.0x over the serial Tarjan's algorithm. It also outperforms the existing OpenMP implementation with a speedup of 1.4x.