@inproceedings{
barghi2023bitgraph,
title={BitGraph: A Framework For Scaling Temporal Graph Queries on {GPU}s},
author={Alexandria Barghi},
booktitle={Temporal Graph Learning Workshop @ NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=B7Wd1K0l4I}
}
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.
BitGraph: A Framework For Scaling Temporal Graph Queries on GPUs
Alexandria Barghi