超级计算机的架构呈现出异构化发展的方向。提升异构系统应用性能的一个重要方面，在于提升应用中通信部分的性能。相对于传统集群，异构系统的数据通信不仅包括节点之间的网络通信，还包括节点内不同类型存储器之间的数据传输；同时，异构系统各个节点的健康状况、网络的使用情况都在时刻发生变化，对通信性能必须针对系统的动态性能模型进行优化。对于非计算机专业背景的应用程序员而言，完成此类优化工作可谓相当困难；而且，即使在一个系统上完成此类优化工作，到另一个体系结构不同的其他系统，往往需要重新考虑此类优化问题。本项目针对大型异构系统（包括GPU加速系统、MIC加速系统和国产众核系统）多进程/线程参与的复杂通信模式，设计基于新型程序语言机制的通信自动优化方案，解决应用程序员在其上进行编程时数据通信和转置的简化表示和自动优化问题，提升大型异构系统对于应用程序员的易用性，应用于直接法湍流模拟和蛋白质模拟示范应用。

The development in super computers has led to a recent trend in which computing systems become increasingly heterogeneous. In order to improve the application performance on heterogeneous systems, the performance and stability of communication must be enhanced. Compared to the traditional cluster, the data communication of heterogeneous system includes not only the network communication among nodes, but also the data transmission between different types of memory inside a single node. For large heterogeneous systems, the health status of each node and the usage of the network change in time, so the communication must be optimized according to the dynamic model of the system performance. For application programmers without a professional background of computer science, it is quite difficult to complete such optimization job; and, even after the optimization is completed on one system, the whole thing needs to be reconsidered when dealing with another system cherishing a different architecture. This project researches on the complex communication mode of mass processes/threads on large-scale heterogeneous systems (including GPU accelerated systems, MIC accelerated systems and domestic many-core systems), designs an automatic communication optimization scheme based on new programming language mechanism, solves the problem of data communication/transposition representation and its automatic optimization, enhances the usability of large-scale heterogeneous systems for application programmers, and improves the performance of turbulence simulation and protein simulation as two typical examples.