达梦数据库中大规模数据可扩展并行算法
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TP311.13

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Research on Scalable Parallel Algorithm for large scale data in DM database
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    摘要:

    DM数据库中的数据规模大且维度复杂,为了在有限的条件下尽可能满足用户对DM数据库功能的需求,提出一种新的DM数据库中大规模数据可扩展并行算法。不可扩展并行算法包括朴素并行、典型并行与逻辑并行三种处理规则,新算法将这三种处理规则结合起来实现数据自主运算,令每个运算节点均拥有三种处理模式,采用有向图将大规模数据划分为局部数据,并分配到处理器上,通过设置数据处理优先等级,完成流水线形式的数据处理过程,赋予并行算法强大的可扩展性。实验结果表明,新算法具有较强的可扩展性,负债均衡能力强。

    Abstract:

    The DM data in the large scale and complex dimensions, to as much as possible in limited conditions to meet the needs of users of the DM database function, proposes a scalable parallel algorithm of large-scale new data in the DM database. Not a scalable parallel algorithm including simple parallel, parallel and parallel three kinds of typical logic processing rules, a new algorithm of the three kinds of rules to combine data independent operations, so that each computation node has three processing modes, using directed graph divide the large-scale data into local data, and assigned to the processor, through set the priority of data processing, to complete the pipeline in the form of data processing, with strong scalability of parallel algorithms. The experimental results show that the new algorithm has strong scalability and excellent debt balance ability.

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王建永,林俊,方杰韬,等. 达梦数据库中大规模数据可扩展并行算法[J]. 科学技术与工程, 2019, 19(7): .
WANG Jian-yong, LIN Jun, FANG Jie-tao, et al. Research on Scalable Parallel Algorithm for large scale data in DM database[J]. Science Technology and Engineering,2019,19(7).

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历史
  • 收稿日期:2017-10-24
  • 最后修改日期:2017-10-24
  • 录用日期:2018-01-08
  • 在线发布日期: 2019-03-15
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