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Multi-core vs. I/O Wall: The Approaches to Conquer and Cooperate

  • Yansong Zhang
  • Min Jiao
  • Zhanwei Wang
  • Shan Wang
  • Xuan Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6897)

Abstract

Multi-core comes to be the mainstream of processor techniques. The data-intensive OLAP relies on inexpensive disks as massive data storage device, so the enhanced processing power oppose to I/O bottleneck in big data OLAP applications becomes more critical because the latency gap between I/O and multi-core gets even larger. In this paper, we focus on the disk resident OLAP with large dataset, exploiting the power of multi-core processing under I/O bottleneck. We propose optimizations for schema-aware storage layout, parallel accessing and I/O latency aware concurrent processing. On the one hand I/O bottleneck should be conquered to reduce latency for multi-core processing, on the other hand we can make good use of I/O latency for heavy concurrent query workload with multi-core power. We design experiments to exploit parallel and concurrent processing power for multi-core with DDTA-OLAP engine which minimizes the star-join cost by directly dimension tuple accessing technique. The experimental results show that we can achieve maximal speedup ratio of 103 for multi-core concurrent query processing in DRDB scenario.

Keywords

I/O wall OLAP multi-core OLAP processing slots DDTA-JOIN 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yansong Zhang
    • 1
    • 2
  • Min Jiao
    • 1
    • 3
  • Zhanwei Wang
    • 1
    • 3
  • Shan Wang
    • 1
    • 3
  • Xuan Zhou
    • 1
    • 3
  1. 1.DEKE LabRenmin University of ChinaBeijingChina
  2. 2.National Survey Research CenterRenmin University of ChinaBeijingChina
  3. 3.School of InformationRenmin University of ChinaBeijingChina

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