Author: Zen and the Art of Computer Programming
In recent years, with the development of mobile Internet, smart bracelets, and mobile games, the penetration rate of Internet of Things terminal devices has gradually increased, and the demand for computing-intensive tasks such as video processing and image recognition has become increasingly strong. In this case, the high-speed parallel computing capability (Graphics Processing Unit) is particularly important. In order to speed up processing, technology companies choose to deploy systems based on Graphics Processing Unit (GPU), and designing faster and more power-saving algorithms is also a key factor in improving processing efficiency. However, due to many limitations in traditional GPU design, the processing performance is not high enough, such as the limited number of cores supporting multi-threaded execution at the same time, limited bandwidth, etc. Therefore, how to design better GPU parallel algorithms and optimize their performance has become a lot of research. Issues faced by personnel and engineers. This article will analyze and discuss from the following aspects:
① GPU working principle and characteristics; ② GPU programming model; ③ CUDA programming language and operating mechanism; ④ CPU-GPU parallel programming model and process; ⑤ GPU memory access mode; ⑥ GPU architecture design; ⑦ GPU parallel programming optimization method; ⑧ GPU Summary of programming practice experience. Through the research, observation and analysis of the above aspects, this paper attempts to answer the following questions:
1. Why use a GPU? What are its advantages? Where are its flaws? 2. What is CUDA programming language and its operating mechanism? What are its application scenarios? 3. What are the CPU-GPU parallel programming models and processes? What types of algorithms are applicable to each? 4. How to reasonably design GPU parallel algorithms? What principles should be followed? 5. How does GPU architecture design affect parallel performance? What does it mainly include? 6. What are the main optimization methods for GPU parallel programming? What are the respective fields of application? 7. What are the pitfalls, problems and solutions encountered in the practice of GPU programming?