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Event Stream Processing on Heterogeneous System Architecture

Published: 01 July 2019 Publication History

Abstract

Due to the widespread availability of general purpose GPUs, an integration of their processing capabilities into an event stream pipeline presents an exciting opportunity riddled with challenging requirements: Even though the single instruction multiple data (SIMD) model is a natural fit to answer long running event queries on high volume streams, those queries are usually associated with latency requirements that make transferring data to GPUs unfeasible. Traditionally, this challenge is solved through software by scheduling some tasks to the GPU and some to the CPU. However, the assumptions about transfer do not hold for widely adopted integrated GPUs (iGPUs), which directly share memory with the CPU. We develop a prototypical event processing framework based on the Heterogeneous System Architecture (HSA) and show that a variety of new HSA features enable iGPUs to be an affordable accelerator for a wide variety of event processing queries.

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Cited By

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  • (2025)Efficient Event Processing on Modern HardwareScalable Data Management for Future Hardware10.1007/978-3-031-74097-8_3(65-89)Online publication date: 24-Jan-2025
  • (2022)iGPU-Accelerated Pattern Matching on Event StreamsProceedings of the 18th International Workshop on Data Management on New Hardware10.1145/3533737.3535099(1-7)Online publication date: 12-Jun-2022
  • (2022)Query Processing on Heterogeneous CPU/GPU SystemsACM Computing Surveys10.1145/348512655:1(1-38)Online publication date: 17-Jan-2022
  • Show More Cited By

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cover image ACM Conferences
DaMoN'19: Proceedings of the 15th International Workshop on Data Management on New Hardware
July 2019
150 pages
ISBN:9781450368018
DOI:10.1145/3329785
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 July 2019

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Cited By

View all
  • (2025)Efficient Event Processing on Modern HardwareScalable Data Management for Future Hardware10.1007/978-3-031-74097-8_3(65-89)Online publication date: 24-Jan-2025
  • (2022)iGPU-Accelerated Pattern Matching on Event StreamsProceedings of the 18th International Workshop on Data Management on New Hardware10.1145/3533737.3535099(1-7)Online publication date: 12-Jun-2022
  • (2022)Query Processing on Heterogeneous CPU/GPU SystemsACM Computing Surveys10.1145/348512655:1(1-38)Online publication date: 17-Jan-2022
  • (2021)An Energy-Efficient Stream Join for the Internet of ThingsProceedings of the 17th International Workshop on Data Management on New Hardware10.1145/3465998.3466005(1-6)Online publication date: 20-Jun-2021

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