An Efficient Data Structure for Dynamic Graph on GPUs

Abstract

There is a growing interest to offload dynamic graph computation to GPU and resort to its high parallel processing ability and larger memory bandwidths compared with CPUs. The existing GPU graph systems usually use compressed sparse row (CSR) as the de-facto structure. However, CSR has a critical weakness for dynamic change due to the large overhead of re-balance process after update. GPMA+ is a state-of-art dynamic PMA-based structure that uses PMA structure and segment-oriented parallel update procedure to address the dynamic weakness of CSR, but it still has a bottleneck on the array expansion. In this paper, we propose an leveled structure (called LPMA) instead of continue array to retain low time complexity and high parallel update and lift the expansion bottleneck of GPMA+. More specifically, we propose a series of optimization techniques, including bottom-up update, top-down update and on-demand hybrid update strategies as well as consistence-guaranteed parallel processing for update-query mixed workloads. We theoretically analyze the benefits of LPMA compared in terms of re-balance cost during updates. Extensive experiments on four large real-life graphs prove the superiority of LPMA compared with the-state-of-arts.