The SABER system for window-based hybrid stream processing with GPGPUs: demo

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 processing engine must execute streaming SQL queries with sufficient data-parallelism to fully utilise the available heterogeneous processors, and decide how to use each processor in the most effective way. Addressing these challenges, we demonstrate S aber , a hybrid high-performance relational stream processing engine for CPUs and GPGPUs. S aber 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, S aber pipelines the transfer of stream data between CPU and GPGPU memory. In this paper, we review the design principles of S aber 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.