  Subgraph matching has garnered increasing attention for its diverse
real-world applications. Given the dynamic nature of real-world graphs,
addressing evolving scenarios without incurring prohibitive overheads has been
a focus of research. However, existing approaches for dynamic subgraph matching
often proceed serially, retrieving incremental matches for each updated edge
individually. This approach falls short when handling batch data updates,
leading to a decrease in system throughput. Leveraging the parallel processing
power of GPUs, which can execute a massive number of cores simultaneously, has
been widely recognized for performance acceleration in various domains.
Surprisingly, systematic exploration of subgraph matching in the context of
batch-dynamic graphs, particularly on a GPU platform, remains untouched. In
this paper, we bridge this gap by introducing an efficient framework, GAMMA
(GPU-Accelerated Batch-Dynamic Subgraph Matching). Our approach features a
DFS-based warp-centric batch-dynamic subgraph matching algorithm. To ensure
load balance in the DFS-based search, we propose warp-level work stealing via
shared memory. Additionally, we introduce coalesced search to reduce redundant
computations. Comprehensive experiments demonstrate the superior performance of
GAMMA. Compared to state-of-the-art algorithms, GAMMA showcases a performance
improvement up to hundreds of times.
