Meerkat: A Framework for Dynamic Graph Algorithms on GPUs - Concessao, Kevin Jude; Cheramangalath, Unnikrishnan; Dev, Ricky; Nasre, Rupesh (International journal of parallel programming, 2024)
Graph algorithms are challenging to implement due to their varying topology and irregular access patterns. Real-world graphs are dynamic in nature and routinely undergo edge and vertex additions, as well as, deletions. Typical examples of dynamic graphs are social networks, collaboration networks, and road networks. Applying static algorithms repeatedly on dynamic graphs is inefficient. Further, due to the rapid growth of unstructured and semi-structured data, graph algorithms demand efficient parallel processing. Unfortunately, we know only a little about how to efficiently process dynamic graphs on massively parallel architectures such as GPUs. Existing approaches to represent and process dynamic graphs are either not general or are inefficient. In this work, we propose a graph library for dynamic graph algorithms over a GPU-tailored graph representation and exploits the warp-cooperative work-sharing execution model . The library, named Meerkat, builds upon a recently proposed dynamic graph representation on GPUs. This representation exploits a hashtable-based mechanism to store a vertex’s neighborhood. Meerkat also enables fast iteration through a group of vertices, a pattern common and crucial for achieving performance in graph applications. Our framework supports dynamic edge additions and edge deletions, along with their batched versions. Based on the efficient iterative patterns encoded in Meerkat, we implement dynamic versions of popular graph algorithms such as breadth-first search, single-source shortest paths, triangle counting, PageRank, and weakly connected components. We evaluated our implementations over the ones in other publicly available dynamic graph data structures and frameworks: GPMA , Hornet , and faimGraph . Using a variety of real-world graphs, we observe that Meerkat significantly improves the efficiency of the underlying dynamic graph algorithm, outperforming these frameworks.