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摘要:
智能卫星技术对卫星时间序列数据挖掘提出了越来越多的需求。通常卫星数据计算量都非常大，若串行执行则需要较长时间。以卫星异变过程多类型特征分析过程为典型代表，针对窗口划分与向量相似度计算、特征提取、傅里叶变换、聚类等常见数据挖掘操作，探讨了在多核CPU和GPU的典型异构计算节点中对时序数据挖掘过程进行并行优化的多种策略，包括向量化方法、多进程方法、GPU计算等方法。对这几种优化策略的适用情况进行了实验分析对比。结果表明，针对不同任务情况综合使用多种优化策略具有显著提升效果。
Abstract:Intelligent satellite technology requires more and more data mining operations for satellite time series data. Usually, satellite data amount is very big that needs a lot of computation, so it will take a very long time to complete the computation in serial program. The satellite anomaly process multi-features analysis procedure is such a typical representation, which performs many common data mining operations, including windows segmentation, computation of vector similarity, feature extraction, Fourier transformation, and cluster-ing. The paper discusses several speed-up and parallel optimization strategies for a time series data mining procedure on a typical heterogeneous computing node with multi-cores CPUs and GPUs, including vector optimization, multi-process parallelization, and GPU computation. We test and compare these optimization strategies in different usage conditions. The experiment results show that the combined use of them can achieve obvious efficiency improvement for different task.
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Key words:
- aerospace big data /
- data mining /
- intelligent satellite /
- parallelization /
- GPU
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图 3 基于多核CPU并行的相似度列表获取流程
Figure 3. Acquisition flowchart of similarity list based on multi-core CPU parallelization
图 4 多窗口向量相似度获取耗时
Figure 4. Time consumption of similarity acquisition of multiple window vectors
图 5 基于GPU并行的相似度计算流程
Figure 5. Flowchart of similarity calculation based onGPU parallelization
图 6 单一窗口向量相似度计算耗时
Figure 6. Time consumption of single window vector similarity calculation
图 7 不同大小窗口的特征提取耗时
Figure 7. Time consumption of feature extraction from different sizes of window
图 8 不同方法优化后的特征计算效率
Figure 8. Characteristic calculation efficiency after optimization by different methods
图 9 并行优化前后的聚类效率
Figure 9. Clustering efficiency before and after parallel optimization
表 1 自适应获取周期算法串行代码优化前后耗时对比
Table 1. Comparison of adaptive cycle achieving algorithm's time consumption before and after serial optimization
数据大小 优化前耗时/s 优化后耗时/s 加速比 221 280 25.150 8 1.625 6 15.5 490 440 127.252 5 2.181 4 58.3 1 028 760 562.171 5 2.592 3 216.9 2 105 400 2 306.469 6 3.755 2 614.2 6 094 920 19 213.119 3 6.079 7 3 160.2 12 238 320 78 204.042 6 16.800 1 4 655.0 
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表 2 不同方法优化前后耗时结果对比
Table 2. Comparison of time consuming results before and after different optimization methods
偏移量δ 耗时/s 无优化 串行优化 并行最优 10 95.285 1 33.591 8 18.403 6 30 58.576 9 11.697 2 10.048 8 90 19.269 7 4.038 3 5.716 7 180 9.632 6 1.975 2 4.020 8
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