Multi-process parallel computing of basic statistics under Big Data of national geographic conditions

High precision and large amount of data are the characteristics of national geographic conditions monitoring data.With the development of the new hardware architecture(multi-core computing, cluster computing,etc.)and constant upgrade of the computer resources,traditional data processing techniques and serial computing technology are difficult to meet the requirements of Big Data processing for high-precise national geographic conditions monitoring.To solve this problem,the paper proposed a multiprocess parallel computing method based on TPL and Named Pipes technology.The results showed that this approach could improve CPU utilization with computing speed increasing by 12times.Moreover,the method could realize the load balancing and extendibility of compute nodes and effectively improve the processing efficiency of national geographic conditions monitoring data.