关键词: 源到源编译, 统一计算架构（CUDA）, 剖分, 渐近拟合优化

Abstract: Aiming at addressing the problem of the inadequate performance optimization after developing and porting of application on CPU-GPU heterogeneous parallel system, a new approach for CPU-GPU system is proposed, which combines asymptotic fitting optimization with source-to-source compiling technique. This approach can translate C code that inserts directives into CUDA code, and profile the generated code several times. Besides, the approach can realize the source-to-source compiling and optimization of the generated code automatically, and a prototype system based on the approach is realized in this paper as well. Functionality and performance evaluations of the prototype show that the generated CUDA code is functionally equivalent to the original C code while its improvement in performance is significant. When compared with CUDA benchmark, the performance of the generated CUDA code is obviously better than codes generated by other source-to-source compiling technique.

Key words: source-to-source compile, Compute Unified Device Architecture（CUDA）, profiling, asymptotic fitting optimization