周伟, 施宁, 王健, 汪群山. 基于GPU-CPU流水线的雷达回波快速聚类[J]. 微电子学与计算机, 2012, 29(4): 71-75.
引用本文: 周伟, 施宁, 王健, 汪群山. 基于GPU-CPU流水线的雷达回波快速聚类[J]. 微电子学与计算机, 2012, 29(4): 71-75.
ZHOU Wei, SHI Ning, WANG Jian, WANG Qun-shan. Fast Clustering of Radar Reflectivity Data Using GPU-CPU Pipeline Scheme[J]. Microelectronics & Computer, 2012, 29(4): 71-75.
Citation: ZHOU Wei, SHI Ning, WANG Jian, WANG Qun-shan. Fast Clustering of Radar Reflectivity Data Using GPU-CPU Pipeline Scheme[J]. Microelectronics & Computer, 2012, 29(4): 71-75.

基于GPU-CPU流水线的雷达回波快速聚类

Fast Clustering of Radar Reflectivity Data Using GPU-CPU Pipeline Scheme

  • 摘要: 提出了基于GPU-CPU流水线的雷达回波快速聚类方法.该方法利用GPU与CPU异步执行的特征, 将聚类的各步骤组织成流水线, 大大的挖掘了聚类全过程的的并行性.实验表明, 引入这种GPU-CPU流水线机制后, 该方法比一般策略的基于GPU的并行聚类算法性能有38%的提升, 而相对于传统的CPU上的串行程序, 获得了47x的加速比, 满足了气象实时分析应用中的实时性要求.

     

    Abstract: In our meteorological application, the clustering algorithm was adopted for analysis and processing of radar reflectivity data.While facing problems of large scale of dataset and high dimension of feature vector, the clustering algorithm is too time-consuming to satisfy the real-time constraint in our applications.This paper proposes a parallelized clustering algorithm using GPU-CPU pipeline scheme to solve this problem.In our method, we utilized the feature of asynchronous execution between GPU and CPU, and organized the process of clustering into pipeline-style, with which we can largely exploit the parallelism in algorithm.The experimental results show that our GPU-CPU pipeline based parallelized clustering algorithm outperform normally parallelized clustering algorithm using CUDA without GPU-CPU pipeline by 38%.Compared to the serial code on CPU, out approach can achieve a 47x performance improvement, which makes it satisfy the requirements of real-time applications.

     

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