Heterogeneous architectures that combine multi-core CPUs with
many-core GPGPUs have the potential to improve the performance
of data-intensive stream processing applications. Yet, a stream pro-
cessing engine must execute streaming SQL queries with sufficient
data-parallelism to fully utilise the available heterogeneous proces-
sors, and decide how to use each processor in the most effective
way. Addressing these challenges, we demonstrate SABER, a
hybrid high-performance relational stream processing engine for
CPUs and GPGPUs. SABER executes window-based streaming SQL queries in a data-parallel fashion and employs an adaptive scheduling strategy to balance the load on the different types of processors. To hide
data movement costs, SABER pipelines the transfer of stream data
between CPU and GPGPU memory. In this paper, we review the
design principles of SABER in terms of its hybrid stream processing
model and its architecture for query execution. We also present a
web front-end that monitors processing throughput.