**[Objective]** In order to quickly analyze the performance of the supercomputing system and accelerate the optimization of HPL benchmark tests, this paper analyzes the main influencing factors of HPL and establishes a related parallel computing model. **[Methods]** Based on the parallel optimization test results of the Sugon advanced computing system HPL benchmark, the method of combining theoretical analysis and experimental verification is used to analyze the HPL efficiency upper limit, fast prediction, and influence of different parameters, on which the corresponding parallel calculations model is established. **[Results]** Compared with the test results of the Sugon advanced computing system, the prediction results are in good agreement with the actual measurement results, indicating the balance between factors such as computing performance and tasks, the ratio of matrix operations to HPL calculation, the efficiency of matrix operations, the utilization of matrix operation library functions, network transmission and so on can largely reflect the calculation efficiency of the HPL of the supercomputing system. Besides, the matrix operation efficiency of the acceleration card is directly proportional to the efficiency of the HPL. **[Limitations]** At present, the design of parallel computing models are not comprehensively considered, and how the stability requirements of a large-scale computing system affects its performance needs further studies. **[Conclusions]** Parallel computing models based on different forecasting requirements have important guiding significance for HPL benchmark performance prediction and parallel optimization.