Exploration of high performance computing model acceleration and tuning methods for domestic GPUs

With the continuous growth of the demand for computing power in the fields of artificial intelligence, big data and scientific computing, high-performance computing has become a key support for the development of national science and technology. In recent years, domestic GPUs, as an important hardware foundation to enhance the level of autonomy and control of arithmetic power, have gradually emerged in applications such as deep learning and scientific simulation. Due to factors such as hardware architecture differences, immature programming ecology, and lack of performance tuning tools, there is still much room for improving the operation efficiency of existing HPC models on domestic GPU platforms.In this paper, we systematically study the acceleration methods and performance tuning strategies of HPC models on domestic GPU platforms. The mainstream architecture and programming framework of domestic GPUs are analyzed to provide a theoretical basis for subsequent optimization. Based on the techniques of parallel computing, memory access optimization and instruction-level tuning, a set of model acceleration strategies for domestic GPUs is constructed and experimentally verified by typical computing models. A performance tuning method and evaluation system for domestic GPUs are proposed, realizing closed-loop optimization from code analysis to resource allocation.The results show that the acceleration and tuning methods proposed in this paper can significantly improve the execution efficiency of domestic GPUs in high-performance computing tasks, and have good generalizability and promotion value. This study provides a strong support for promoting the development of domestic GPU ecosystem and its wide application in complex computing scenarios.