=Paper=
{{Paper
|id=Vol-1670/paper-49
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1670/paper-49.pdf
|volume=Vol-1670
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1670/paper-49.pdf</pdf>
<pre>
 SABER: Window-Based Hybrid Stream Processing for
          Heterogeneous Architectures?

         Alexandros Koliousis† , Matthias Weidlich†‡ , Raul Castro Fernandez† ,
               Alexander L. Wolf† , Paolo Costa] , and Peter Pietzuch†
       {akolious, mweidlic, rc3011, alw, costa, prp}@imperial.ac.uk
   † Imperial College London               ‡ Humboldt-Universität zu Berlin                ] Microsoft Research




Abstract
Stream processing systems found wide-spread application in domains such as credit
fraud detection, urban traffic management, and click stream analytics. These systems
process continuous streams of input data in an online manner, aiming at maximising
processing throughput while staying within acceptable latency bounds. Heterogeneous
architectures that combine multi-core CPUs with many-core GPGPUs have the potential
to improve the performance of stream processing engines. 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 present S ABER, a hybrid high-performance re-
lational stream processing engine for CPUs and GPGPUs. It executes window-based
streaming SQL queries following a hybrid execution model. Specifically, S ABER incor-
porates the following innovations:
  ◦ It features a hybrid stream processing model based on query tasks, each comprising a
     batch of stream data and a query operator. Instead of relying on offline performance
     models to select the processor on which to run a query operator, S ABER employs an
     adaptive heterogeneous lookahead scheduling strategy to balance the load on the
     different types of processors.
  ◦ It provides window-aware task processing, supporting sliding window semantics
     in the presence of fixed-sized batches. S ABER ensures result correctness after the
     out-of-order processing of tasks by first buffering and then incrementally releasing
     the results as tasks finish execution.
  ◦ It exploits pipelined stream data movement to the GPGPU that interleaves data
     movement and task execution, thereby maintaining high utilisation of the PCIe
     bandwidth.
An experimental comparison against state-of-the-art engines shows that S ABER increases
processing throughput while maintaining low latency for a wide range of streaming SQL
queries with both small and large window sizes.

 ? Published as: A. Koliousis, M. Weidlich, R. C. Fernandez, A. L. Wolf, P. Costa, and P. Pietzuch. SABER: Window-based

   hybrid stream processing for heterogeneous architectures. In F. Özcan, G. Koutrika, and S. Madden, editors, Proceedings
   of the 2016 SIGMOD Conference, San Francisco, CA, USA, June 26 - July 01, 2016, pages 555–569, ACM.

</pre>