  Regular path queries (RPQs) in graph databases are bottlenecked by the memory
wall. Emerging processing-in-memory (PIM) technologies offer a promising
solution to dispatch and execute path matching tasks in parallel within PIM
modules. We present Moctopus, a PIM-based data management system for graph
databases that supports efficient batch RPQs and graph updates. Moctopus
employs a PIM-friendly dynamic graph partitioning algorithm, which tackles
graph skewness and preserves graph locality with low overhead for RPQ
processing. Moctopus enables efficient graph update by amortizing the host
CPU's update overhead to PIM modules. Evaluation of Moctopus demonstrates
superiority over the state-of-the-art traditional graph database.
