With the development of infrared and laser technology, the computational demand for high-resolution atmospheric gas absorption spectra is continuously increasing. Instruments featuring ultra-high spectral resolution, exemplified by the Tropospheric Emission Spectrometer (TES), have already been developed internationally. To retrieve valid information from high spectral resolution devices, rapid computation of atmospheric transmittance at higher spectral resolutions is imperative. At the same time, in specialized application fields such as the simulation of high-altitude flying object plumes, reliance on specialized spectral databases like the high-temperature molecular spectroscopic database (HITEMP) becomes essential for high spectral resolution calculations. However, some specialized models addressing these challenges have already been developed abroad, and domestic resources for addressing the above issues are still relatively scarce. In atmospheric radiation transmission calculations, the computation of transmittance presents a significant challenge. Currently, the line-by-line integration method offers the highest calculation accuracy, up to 0.5%, but it is extremely time-consuming. Consequently, calculating absorption coefficients for broader bands imposes numerous practical engineering usage restrictions. In recent years, graphics processing unit (GPU) parallel computing technology has been widely applied in scientific computation. We design a general high-resolution atmospheric spectral line parallel computing model based on GPU, which has increased the computing speed by one to three orders of magnitude. On this basis, combined with the correlated K distribution algorithms, a correlated K distribution coefficient table with a spectral resolution of 1 cm^－1 has been constructed, achieving a parameterized representation of line-by-line integration calculation results and enhancing the universality of computational products. Our work endeavors to present a novel technical approach for high-resolution, rapid radiation transmission calculations under standard atmospheric conditions and high-temperature gases.

We first design a parallel computation for both the thermodynamic state and spectral line calculations based on the computational characteristics of the line-by-line integration method. Then, through a central processing unit (CPU) +GPU heterogeneous platform, the design processes for both the CPU and GPU sides are optimized by employing parallel computing techniques such as shared memory optimization, atomic operations, loop unrolling, and pre-processing of complex calculations, thereby constructing an efficient parallel computing model. Subsequently, this model is utilized to verify the accuracy of absorption cross-section calculations under atmospheric conditions and radiance calculations under non-uniform paths, demonstrating the computational accuracy of the model. Tests and analyses are also conducted on the parallel computation between spectral lines and under various thermodynamic states, confirming the model’s computational efficiency. Furthermore, based on this model and employing the Malkmus band model parameter fitting method, we conduct a correlated K distribution coefficient table with a resolution of 1 cm^－1, enabling rapid atmospheric transmittance calculation under non-GPU hardware conditions. Finally, we compare the transmittance calculated using the correlated K distribution coefficient table with that calculated by the line-by-line integration method, verifying the accuracy of the correlated K distribution table.

We design a universal high-resolution atmospheric spectral line parallel computing model based on GPU, according to the computational characteristics of the line-by-line integration method, which achieves an acceleration effect of one to three orders of magnitude (Table 3). Without compromising computation accuracy, the method significantly improves the efficiency of atmospheric spectral line calculation, providing a powerful tool for atmospheric radiation transmission calculation. On this basis, combined with the correlated K distribution coefficient table constructed by the Malkmus band model parameter fitting method, it is compared with the calculation results of the line-by-line integration method under the same computing conditions, also demonstrating good computational accuracy (Fig. 9).

We combine line-by-line integration with GPU parallel computing, utilizing techniques such as shared memory optimization, atomic operations, loop unrolling, and the preprocessing of complex computations to construct an efficient parallel computing model. This model facilitates rapid calculations of high-resolution absorption coefficients, atmospheric transmittance, and other typical radiative transfer results in environments ranging from 1 to 5000 K. Subsequently, we use the model to calculate CO₂ infrared radiation problems in atmospheric and high-temperature environments and conduct accuracy verification, followed by an in-depth analysis of the model’s parallel acceleration capability in different environments. The research results show that the designed parallel computing model can accurately produce the required computation results and achieve a speed-up ratio of over 800 times when processing large-scale spectral line calculations. Finally, by integrating the parametric method of the Malkmus spectral band model, a new process for quickly generating correlated K distribution coefficient tables is realized. This approach, distinct from previous research, extends the parallel computing achievements to devices without GPU or with limited memory. This technological approach not only expands the application field of existing technologies but also provides a new and efficient solution for research and practical applications in related fields.