BitGraph: A Framework For Scaling Temporal Graph Queries on GPUs
Keywords: graph analytics, graph computing, GPUs, graph databases, time series, temporal graphs, sparse matrices, query languages
TL;DR: Describes the first official release of the BitGraph framework, which accelerates graph query language processing using GPUs
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.
Format: Long paper, up to 8 pages. If the reviewers recommend it to be changed to a short paper, I would be willing to revise my paper to fit within 4 pages.
Submission Number: 17
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