  Given the massive growth in the volume of spatial data, there is a great need
for systems that can efficiently evaluate spatial queries over large data sets.
These queries are notoriously expensive using traditional database solutions.
While faster response times can be attained through powerful clusters or
servers with large main-memory, these options, due to cost and complexity, are
out of reach to many data scientists and analysts making up the long tail.
  Graphics Processing Units (GPUs), which are now widely available even in
commodity desktops and laptops, provide a cost-effective alternative to support
high-performance computing, opening up new opportunities to the efficient
evaluation of spatial queries. While GPU-based approaches proposed in the
literature have shown great improvements in performance, they are tied to
specific GPU hardware and only handle specific queries over fixed geometry
types.
  In this paper we present SPADE, a GPU-powered spatial database engine that
supports a rich set of spatial queries. We discuss the challenges involved in
attaining efficient query evaluation over large datasets as well as portability
across different GPU hardware, and how these are addressed in SPADE. We
performed a detailed experimental evaluation to assess the effectiveness of the
system for wide range of queries and datasets, and report results which show
that SPADE is scalable and able to handle data larger than main-memory, and its
performance on a laptop is on par with that other systems that require clusters
or large-memory servers.
