面向高速列车监测数据的并行解压缩算法

Parallel decompression algorithm for high-speed train monitoring data

高速列车在运行时产生的实时监测数据通常用变长编码压缩技术进行处理,以便于传输和存储。然而这种方法会使得压缩数据内部结构复杂,导致相应的数据解压缩过程只能遵照压缩数据的组成顺序进行,效率较低。为提升高速列车监测数据的解压缩效率,借助推测技术,提出一种面向高速列车监测数据的并行解压缩算法。首先,研究高速列车监测数据的结构特征,分析影响数据划分的内部依赖;其次,利用推测技术消解内部依赖后,对数据进行试探性划分;然后在分布式计算环境中对划分结果并行地进行解压;最后将并行解压缩结果合并起来,从而提高针对高速列车监测数据的解压缩效率。实验结果表明,在由7个计算节点组成的计算集群上,与串行算法相比,所提推测并行算法的加速比为3左右,展现了该算法良好的性能,可见该算法能够显著提高针对列车监测数据的解压缩效率。

The real-time monitoring data generated by high-speed trains during running are usually processed by variable-length coding compression technology,which is convenient for transmission and storage.However,this method will complicate the internal structure of the compressed data,so that the corresponding data decompression process must follow the composition order of the compressed data,which is inefficient.In order to improve the decompression efficiency of highspeed train monitoring data,a parallel decompression algorithm for high-speed train monitoring data was proposed with the help of the speculation technology.Firstly,the structural characteristics of high-speed train monitoring data were studied,and the internal dependence that affects data division was analyzed.Secondly,the speculation technology was used to clean up internal dependence,and then,the data were divided into different parts tentatively.Thirdly,the division results were decompressed in a distributed computing environment in parallel.Finally,the parallel decompression results were combined together.Through this way,the decompression efficiency of high-speed train monitoring data was improved.Experimental results showed that on the computing cluster composed of 7 computing nodes,compared with the serial algorithm,the speedup of the proposed speculative parallel algorithm was about 3,showing a good performance of this algorithm.It can be seen that this algorithm can improve the monitoring data decompression efficiency significantly.