research-article

SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures

Authors:

Alexandros Koliousis,

Matthias Weidlich,

Raul Castro Fernandez,

Alexander L. Wolf,

Paolo Costa,

Peter PietzuchAuthors Info & Claims

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

Pages 555 - 569

https://doi.org/10.1145/2882903.2882906

Published: 14 June 2016 Publication History

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Editorial Notes

Computationally Replicable. The experimental results of this paper were replicated by a SIGMOD Review Committee and were found to support the central results reported in the paper. Details of the review process are found here

Abstract

Modern servers have become heterogeneous, often combining multi-core CPUs with many-core GPGPUs. Such heterogeneous architectures have the potential to improve the performance of data-intensive stream processing applications, but they are not supported by current relational stream processing engines. For an engine to exploit a heterogeneous architecture, it must execute streaming SQL queries with sufficient data-parallelism to fully utilise all available heterogeneous processors, and decide how to use each in the most effective way. It must do this while respecting the semantics of streaming SQL queries, in particular with regard to window handling.

We describe 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 using all available CPU and GPGPU cores. Instead of statically assigning query operators to heterogeneous processors, Saber employs a new adaptive heterogeneous lookahead scheduling strategy, which increases the share of queries executing on the processor that yields the highest performance. To hide data movement costs, Saber pipelines the transfer of stream data between CPU and GPGPU memory. Our experimental comparison against state-of-the-art engines shows that Saber increases processing throughput while maintaining low latency for a wide range of streaming SQL queries with both small and large window sizes.

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References

[1]

Apache Samza. http://samza.apache.org/. Last access: 09/02/16.

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