The I/O performance of new type storage devices is usually an order of magnitude higher than that of traditional Solid State Disk (SSD). However, simply replacing SSD with new type storage device will not significantly improve the performance of distributed file system. This means that the current distributed file system cannot give full play to the performance of new type storage devices. To solve the problem, the data writing process and transmission process of Hadoop Distributed File System (HDFS) were analyzed quantitatively. Through quantitative analysis of the time consumptions of different stages of HDFS writing process, the most time-consuming data transmission between nodes was found in each stage of writing data. Therefore, the corresponding optimization strategy was proposed, that is, the processes of data transmission and processing were parallelized by using asynchronous write. So that the processing stages of different data packets were parallel to each other, shortening the total processing time of data writing, thereby the write performance of HDFS was improved. Experimental results show the proposed scheme improves the HDFS write throughput by 15%-24%, and reduces the overall write execution time by 28%-36%.