GPU-Accelerated Batch-Dynamic Subgraph Matching

TL;DR

This paper introduces GAMMA, a GPU-based framework for efficient batch-dynamic subgraph matching that significantly outperforms existing serial approaches by leveraging parallel processing and novel load balancing techniques.

Contribution

The paper presents a novel GPU-accelerated framework with a warp-centric DFS algorithm, warp-level work stealing, and coalesced search for batch-dynamic subgraph matching.

Findings

Performance improved up to hundreds of times over state-of-the-art

Effective load balancing with warp-level work stealing

Reduced redundant computations through coalesced search

Abstract

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.