GPU-accelerated Structural Diversity Search in Graphs

The problem of structural diversity search has been widely studied recently, which aims to find out the users with the highest structural diversity in social networks. The structural diversity of a user is depicted by the number of social contexts inside his/her contact neighborhood. Three structural diversity models based on cohesive subgraph models (e.g., k-sized component, k-core, and k-truss), have been proposed. Previous solutions only focus on CPU-based sequential solutions, suffering from several key steps of that cannot be highly parallelized. GPUs enjoy high-efficiency performance in parallel computing for solving many complex graph problems such as triangle counting, subgraph pattern matching, and graph decomposition. In this paper, we provide a unified framework to utilize multiple GPUs to accelerate the computation of structural diversity search under the mentioned three structural diversity models. We first propose a GPU-based lock-free method to efficiently extract ego-networks in CSR format in parallel. Secondly, we design detailed GPU-based solutions for computing k-sized component-based, k-core-based, and also k-truss-based structural diversity scores by dynamically grouping GPU resources. To effectively optimize the workload balance among multiple GPUs, we propose a greedy work-packing scheme and a dynamic work-stealing strategy to fulfill usage. Extensive experiments on real-world datasets validate the superiority of our GPU-based structural diversity search solutions in terms of efficiency and effectiveness.