A Parallel Optimization Model for Massive Data Stream Application
DOI:
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    While computing is entering a new phase in which CPU improvements are driven by the addition of multiple cores on a single chip, rather than higher frequencies. Parallel processing on these systems is in a primitive stage, and requires the explicit use and knowledge of underlying thread architecture. Based on the features of massive data stream application, this paper proposes three-level pipelining programming model of multithreading system, which realizes the new synchronization mechanism with no contention of shared structures and is capable to provide differential service for data streams. Then the paper applies the new model to remote sensing information processing system and backbone network intrusion detection system, and evaluates the improved system on several multicore platforms. In performance analysis, the optimized effects of backbone network intrusion detection system are evaluated in several aspects of throughput scalability on both SPARC T1 and x86 platforms, the impacts of different multithreading mapping methods on throughput, and the comparison of response time and service quality before and after optimization. The experimental results show that the system throughput has good scalability on both platforms, the values of response time are greatly improved and the prioritized streams achieve better response time with the differential service mechanism.

    Reference
    Related
    Cited by
Get Citation

孙小涓,孙凝晖,雷 斌.一种海量数据流应用并行优化模型.软件学报,2009,20(zk):23-33

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:July 01,2008
  • Revised:April 02,2009
  • Adopted:
  • Online:
  • Published:
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063