Implementation and optimization of GPU-based relational streaming processing systems

HUANG Hao; LI Zhi-fang; WANG Jia-lun; WENG Chu-liang

doi:10.3969/j.issn.1000-5641.2019.05.015

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Dec. 2019

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HUANG Hao, LI Zhi-fang, WANG Jia-lun, WENG Chu-liang. Implementation and optimization of GPU-based relational streaming processing systems[J]. Journal of East China Normal University (Natural Sciences), 2019, (5): 178-189. doi: 10.3969/j.issn.1000-5641.2019.05.015

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HUANG Hao, LI Zhi-fang, WANG Jia-lun, WENG Chu-liang. Implementation and optimization of GPU-based relational streaming processing systems[J]. Journal of East China Normal University (Natural Sciences), 2019, (5): 178-189. doi: 10.3969/j.issn.1000-5641.2019.05.015

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Implementation and optimization of GPU-based relational streaming processing systems

doi: 10.3969/j.issn.1000-5641.2019.05.015

School of Data Science and Engineering, East China Normal University, Shanghai 200062, China

Received Date: 2019-07-29
Publish Date: 2019-09-25

Abstract

Abstract

State-of-the-art CPU-based streaming processing systems support complex queries on large-scale datasets. However, limited by CPU computational capability, these systems suffer from the performance tradeoff between throughput and response time, and cannot achieve the best of both. In this paper, we propose a GPU-based streaming processing system, named Serval, that co-utilizes CPU and GPU resources and efficiently processes streaming queries by micro-batching. Serval adopts the pipeline model and uses streaming execution cache to optimize throughput and response time on large scale datasets. To meet the demands of various scenarios, Serval implements multiple tuning policies by scaling the micro-batch size dynamically. Experiments show that a single-server Serval outperforms a 3-server distributed Spark Streaming by 3.87x throughput with a 91% response time on average, reflecting the efficiency of the optimization.
- streaming processing system,
- relational query,
- GPU database

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References(18)

References

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