Abstract: The efficiency of numerical calculation of underwater acoustic propagation is one of the key factors in various applications of underwater acoustic science. As a numerical method for solving diffe- rential equations, spectral method has the advantages of high accuracy and fast convergence. Therefore, using normal wave-spectrum method to solve underwater acoustic propagation equations has attracted the attentions of many scholars in recent years. However, the spectral method requires more computation, and the computational efficiency is still difficult to meet the real-time requirements when solving large-scale underwater acoustic propagation problems. Therefore, it is necessary to use a high- performance computing system to carry out performance optimization and program parallelization research on the typical spectral method to calculate the underwater sound propagation program to improve the computing performance. Firstly, the calculation flow and hotspot functions of the program are analyzed. Then, the optimization methods of compiler options, calling high-performance math library MKL, memory access optimization, and reduced computation are studied. Finally, multi-thread parallel acceleration processing is carried out on the many-core high-performance computing platform. Through testing and evaluation on the Tianhe-2 many-core platform, the results show that the running time of the final parallel optimized version of the deep-sea waveguide calculation example is reduced from 584 seconds to 24 seconds compared with the original serial version, which is 23.98 times faster. The calculation time verifies the validity of the used method , and is of great significance to the calculation of the water sound field in the ocean. Further analysis shows that these optimization and parallelization methods also have reference and reference significance for other scientific and engineering numerical calculation problems of the same type on the same platform.

Key words: spectral method, serial optimization, multi-threaded, parallel optimization, speedup