华东师范大学学报（自然科学版） >

2019 , Vol. 2019 >Issue 5: 178 - 189

DOI: https://doi.org/10.3969/j.issn.1000-5641.2019.05.015

新时期数据管理技术

基于GPU的关系型流处理系统实现与优化

  • 黄皓 ,
  • 李志方 ,
  • 王嘉伦 ,
  • 翁楚良
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  • 华东师范大学 数据科学与工程学院, 上海 200062
黄皓,男,硕士研究生,研究方向为内存型数据库.E-mail:haohuang@stu.ecnu.edu.cn.

收稿日期: 2019-07-29

  网络出版日期: 2019-10-11

基金资助

国家重点研发计划（2018YFB1003400）

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Implementation and optimization of GPU-based relational streaming processing systems

  • HUANG Hao ,
  • LI Zhi-fang ,
  • WANG Jia-lun ,
  • WENG Chu-liang
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  • School of Data Science and Engineering, East China Normal University, Shanghai 200062, China

Received date: 2019-07-29

  Online published: 2019-10-11

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摘要

现有的基于CPU的流处理系统在功能上已支持在大规模数据集上的复杂分析查询，但由于CPU计算能力与特性的限制，无法在性能上同时满足高吞吐量和低响应时间的要求.本文提出一种基于GPU的流处理系统框架Serval，通过充分利用CPU-GPU异构资源，实现了关系型流查询的高效处理.Serval框架采用流水线模型和流执行缓存技术以优化吞吐量和响应时间，并实现多种调优策略以适应不同场景.实验表明，单节点Serval的吞吐量与响应时间性能均优于现有GPU数据库MapD和三节点分布式服务器上的Spark Streaming.

关键词： 流处理系统; 关系型查询; GPU~数据库

本文引用格式

黄皓 , 李志方 , 王嘉伦 , 翁楚良 . 基于GPU的关系型流处理系统实现与优化[J]. 华东师范大学学报（自然科学版）, 2019 , 2019(5) : 178 -189 . DOI: 10.3969/j.issn.1000-5641.2019.05.015

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.

Key words： streaming processing system; relational query; GPU database

参考文献

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