JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

ARL中Clean算法的并行化研究

刘慧慧 ,  闻萌莎 ,  钱慎一 ,  吴怀广 ,  张伟伟 ,  李代祎

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刘慧慧, 闻萌莎, 钱慎一, 等. ARL中Clean算法的并行化研究[J]. 轻工学报, 2019, 34(2): 88-94. doi: 10.3969/j.issn.2096-1553.2019.02.012
引用本文: 刘慧慧, 闻萌莎, 钱慎一, 等. ARL中Clean算法的并行化研究[J]. 轻工学报, 2019, 34(2): 88-94. doi: 10.3969/j.issn.2096-1553.2019.02.012
shu
LIU Huihui, WEN Mengsha, QIAN Shenyi, et al. Research on parallelization of Clean algorithm in ARL[J]. Journal of Light Industry, 2019, 34(2): 88-94. doi: 10.3969/j.issn.2096-1553.2019.02.012
Citation: LIU Huihui, WEN Mengsha, QIAN Shenyi, et al. Research on parallelization of Clean algorithm in ARL[J]. Journal of Light Industry, 2019, 34(2): 88-94. doi: 10.3969/j.issn.2096-1553.2019.02.012
shu

ARL中Clean算法的并行化研究

  • 1. 郑州轻工业大学 计算机与通信工程学院, 河南 郑州 450001;

  • 2. 华东师范大学 计算机科学与软件工程学院, 上海 200241

    • 作者简介: 刘慧慧(1994-),女,河南省郑州市人,郑州轻工业大学硕士研究生,主要研究方向为算法优化与算法并行化.;

  • 基金项目: 国家重点研发计划政府间科技合作项目（2016YFE0100600；2016YFE0100300）

  • 中图分类号: TP301

Research on parallelization of Clean algorithm in ARL

  • 1. College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China;

  • 2. College of Computer Science and Software Engiveering, East China Normal University, Shanghai 200241, China

  • Received Date: 2018-12-13

    CLC number: TP301

  • 摘要: 针对SKA算法参考库ARL中的去卷积算法运行效率低、无法满足海量数据实时处理的问题，提出了CPU和GPU协同工作模式下的并行化Clean算法.该方法将Clean算法中可以并行计算的步骤利用多线程在GPU上并行执行，将无法并行计算的步骤在CPU上串行执行.验证实验结果表明，在数据逐渐增大的过程中，并行化Clean算法比在CPU上的串行处理运行时间显著减少，当图达到4096像素×4096像素时，可以有10倍的提速.这说明并行化Clean算法在处理海量数据时，能够显著提高运算效率.
    Abstract: The deconvolution algorithm in the ARL of the SKA algorithm reference library is inefficient and cannot meet the needs of real-time processing of massive data. The parallelized Clean algorithm in the cooperative working mode of CPU and GPU was proposed. The steps of parallel computing in Clean algorithm were executed in parallel on GPU using multi-threads, and the steps in the Clean algorithm that couldn't be parallelized were executed serially on the CPU. The results showed that the running time of parallel Clean algorithm under CPU and GPU cooperative mode was significantly shorter than that under CPU. When the image size reached 4096×4096, the parallel Clean algorithm GPU cooperative mode could be speeded up by 10 times, which showed that the parallel Clean algorithm could significantly improve the efficiency of operation when dealing with massive data.
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刘慧慧, 闻萌莎, 钱慎一, 等. ARL中Clean算法的并行化研究[J]. 轻工学报, 2019, 34(2): 88-94. doi: 10.3969/j.issn.2096-1553.2019.02.012
引用本文: 刘慧慧, 闻萌莎, 钱慎一, 等. ARL中Clean算法的并行化研究[J]. 轻工学报, 2019, 34(2): 88-94. doi: 10.3969/j.issn.2096-1553.2019.02.012
shu
LIU Huihui, WEN Mengsha, QIAN Shenyi, et al. Research on parallelization of Clean algorithm in ARL[J]. Journal of Light Industry, 2019, 34(2): 88-94. doi: 10.3969/j.issn.2096-1553.2019.02.012
Citation: LIU Huihui, WEN Mengsha, QIAN Shenyi, et al. Research on parallelization of Clean algorithm in ARL[J]. Journal of Light Industry, 2019, 34(2): 88-94. doi: 10.3969/j.issn.2096-1553.2019.02.012
shu

ARL中Clean算法的并行化研究

    作者简介:刘慧慧(1994-),女,河南省郑州市人,郑州轻工业大学硕士研究生,主要研究方向为算法优化与算法并行化.
  • 1. 郑州轻工业大学 计算机与通信工程学院, 河南 郑州 450001;
  • 2. 华东师范大学 计算机科学与软件工程学院, 上海 200241
  • 收稿日期: 2018-12-13
基金项目:  国家重点研发计划政府间科技合作项目（2016YFE0100600；2016YFE0100300）

摘要: 针对SKA算法参考库ARL中的去卷积算法运行效率低、无法满足海量数据实时处理的问题，提出了CPU和GPU协同工作模式下的并行化Clean算法.该方法将Clean算法中可以并行计算的步骤利用多线程在GPU上并行执行，将无法并行计算的步骤在CPU上串行执行.验证实验结果表明，在数据逐渐增大的过程中，并行化Clean算法比在CPU上的串行处理运行时间显著减少，当图达到4096像素×4096像素时，可以有10倍的提速.这说明并行化Clean算法在处理海量数据时，能够显著提高运算效率.

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