  Over the past decade, the landscape of data analytics has seen a notable
shift towards heterogeneous architectures, particularly the integration of GPUs
to enhance overall performance. In the realm of in-memory analytics, which
often grapples with memory bandwidth constraints, the adoption of GPUs has
proven advantageous, thanks to their superior bandwidth capabilities. The
parallel processing prowess of GPUs stands out, providing exceptional
efficiency for data-intensive workloads and outpacing traditional CPUs in terms
of data processing speed. While GPU databases capitalize on these strengths,
there remains a scarcity of comparative studies across different GPU systems.
In light of this emerging interest in GPU databases for data analytics, this
paper proposes a survey encompassing multiple GPU database systems. The focus
will be on elucidating the underlying mechanisms employed to deliver results
and key performance metrics, utilizing benchmarks such as SSB and TPCH. This
undertaking aims to shed light on new avenues for research within the realm of
GPU databases.
