NVIDIA developed a technique of executing general program on GPU, named CUDA (Compute Unified Device Architecture), in 2006. The CUDA programming model allows a group of same instructions to execute on multi-thread simultaneously, which has advantage of parallel programs in reducing the execution time significantly. Although CUDA provides a series of C-like APIs (Application Programming Interface) so that programmers can easy use CUDA language, it still costs certain efforts to be familiar with the development. In this thesis, we propose a tool to automatically translate C programs into corresponding CUDA programs which reduce program development time effectively.

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Due to the limitation of single machine¡¦s computing power, and the aspect of cost, distributed design is getting more and more popular nowadays. The Distributed Shared Memory (DSM) system is one of the most hot topics in this area. Most people are dedicated on designing a library or even a new language, in order to gain higher performance on DSM systems. As a consequence, the programmers are required to learn a new library or language. Even more, they have to handle synchronizations for the distributed environment. In this paper, we propose a design that is compatible with POSIX-Thread Environment. The distributed nature of the system described herein is totally transparent to the programmers.