In order to analyze and predict the performance of CUDA program kernel and guide parallel program design and performance optimization

a performance prediction framework is proposed. This paper starts with the GPU programming model and hardware architecture details

with warp as the research unit. By integrating hardware and software factors closely related to GPU program time

high-level performance-related features such as device parallel space idle degree (DPSID)

number of streaming multiprocessor warp (NSMW) are defined. Based on the above features

a framework for evaluating the execution time of kernel functions under different problem sizes and execution configurations is built for thread load balancing GPU programs. The principle of optimizing configuration parameters of kernel function execution is put forward to guide optimizing program performance. The experimental results show that the average prediction accuracy of the framework is 89% and 94% in the two scenarios

respectively.