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Workshop: Temporal Graph Learning Workshop @ NeurIPS 2023

BitGraph: A Framework For Scaling Temporal Graph Queries on GPUs

Alexandria Barghi

Abstract: Graph query languages have become the standard among data scientists analyzing large, dynamic graphs, allowing them to structure their analysis as SQL-like queries. One of the challenges in supporting graph query languages is that, unlike SQL queries, graph queries nearly always involve aggregation of sparse data, making it challenging to scale graph queries without heavy reliance on expensive indices. This paper introduces the first major release of $\textit{BitGraph}$, a graph query processing engine that uses GPU-acceleration to quickly process Gremlin graph queries with minimal memory overhead, along with its supporting stack, $\textit{Gremlin++}$, which provides query language support in C++, and $\textit{Maelstrom}$, a lightweight library for compute-agnostic, accelerated vector operations built on top of $\textit{Thrust}$. This paper also analyzes the performance of BitGraph compared to existing CPU-only backends applied specifically to temporal graph queries, demonstrating BitGraph's superior scalability and speedup of up to 35x over naive CPU implementations.

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