Automatic Code Tuning for Improving GPU Resource Utilization

Utilizing a GPU to perform general purpose computation is called GPGPU. The high theoretical performance of GPU draws attention to GPGPU. CUDA supplies a platform for the developers of GPU applications. In CUDA programming model, massive threads are allocated to GPU´s calculation units. Besides, CUDA has various kinds of memories on GPU. These memories have different features of access latency, capacity, and so on. Therefore, to produce high-performance GPU programs, developers should consider how to allocate the massive threads to cores and which memory should be used for storing data. Hence, developers should have deep understanding of the GPU architecture and CUDA APIs. To address this problem, we propose an auto tuning framework for GPU programs, and explain an implementation of a preprocessor for the framework, in this paper.