To address the problem of poor software portability and programmability of a graphic processing unit ( GPU ) , and to facilitate the development of parallel programs on GPU, this study proposed a novel directive based compiler guided approach, and then the GPU-S2S, a prototypic tool for automatic source-to-source translation, was implemented through combining automatic mapping with static compilation configuration, which is capable of translating a C sequential program with directives into a compute unified device architecture (CUDA) program. The experimental results show that CUDA codes generated by the GPU-S2S can achieve comparable performance to that of CUDA benchmarks provided by NVIDIA CUDA SDK, and have significant performance improvements compared to its original C sequential codes.%针对图形处理器(GPU)架构下的软件可移植性、可编程性差的问题,为了便于在GPU上开发并行程序,通过自动映射与静态编译相结合,提出了一种新的基于制导语句控制的编译优化方法,实现了一个源到源的自动转化工具GPU-S2S,它能够将插入了制导语句的串行C程序转化为统一计算架构(CUDA)程序.实验结果表明,经GPU-S2S转化生成的代码和英伟达(NVIDIA)提供的基准测试代码具有相当的性能；与原串行程序在CPU上执行相比,转换后的并行程序在GPU上能够获取显著的性能提升.
展开▼
