当前，一些复杂应用同时包含高性能计算和大数据处理两种计算模式，这就要求将两种计算模式统一到一套平台上。然而目前普遍用于大数据处理的分布式集群并不能有效支持高性能计算，因此，本项目试图使面向高性能计算的超级计算机支持大数据处理，从而实现两种计算模式在超级计算机上的融合。在超级计算机上实现大数据处理存在以下三大挑战：①大数据处理应用产生的海量文件将对超级计算机的全局并行文件系统形成巨大的压力；②在融合平台上实现高性能计算作业和大数据处理作业的混合调度面临严峻挑战；③在超级计算机上实现便捷高效的大数据处理还有赖于应用框架的全面革新。针对以上三大挑战，本项目分别从面向大数据处理的全局共享存储系统、高性能计算与大数据处理混合调度系统、基于超级计算机的大数据处理框架三个方面展开研究，构建基于超级计算机的大数据处理支撑平台，最终实现高性能计算与大数据处理在超级计算机上的融合。

In recent years, the complex applications consisting of both high-performance computing and big data processing phases come to emerge gradually. The platform able to support both the two computing models is on demand to support applications of this kind. Generally, the distributed cluster used to support big data processing is unable to support high-performance computing applications efficiently. Therefore, this research aims at facilitating the supercomputer designed for high-performance computing to support big data processing, thus coupling the two types of applications on the supercomputer. There exist three challenges when running big data processing jobs on supercomputer: ①the large amounts of files produced by big data processing introduce so heavy workloads that are beyond the capability of the storage system in supercomputer; ②the hybrid job scheduling taking both high-performance computing and big data processing into account confronts with unimagined challenges; ③the big data processing framework must be redesigned to adapt to the architectural characteristics of supercomputer. By considering the three challenges mentioned above, this work focuses on the research of the big data processing-oriented globally-shared storage system, the hybrid job scheduling of high-performance computing and big data processing, and the big data processing framework based on supercomputer. The technologies involved in the research will help to build a supercomputer-based platform supporting big data processing, and ultimately couple high-performance computing and big data processing on the supercomputer.