Interpreting measured absorption spectroscopy data can require repeated simulations of the expected absorption spectrum to fit the data. In cases of high temperature or broadband spectra, the computational load of the spectral analysis can be expensive due to the large number of individual absorption transitions that contribute to each simulation. We present a Graphics Processing Unit (GPU) Accelerated Absorption Simulator (GAAS) – a fast, hardware-accelerated, line-by-line absorption simulation software for generating absorption spectra based on Voigt and Hartmann-Tran lineshape profiles. We show that GAAS produces the same output spectra as the high-resolution transmission molecular absorption database (HITRAN) Application Programming Interface (HAPI) to within 32-bits of numerical precision for spectra based on both Voigt and Hartmann-Tran profiles. We also measure the performance increase compared to HAPI and demonstrate that GAAS can reduce simulation time by up to 115x for spectra containing many (several thousand or more) absorption transitions. The software is provided as an open-source python library which is built around an OpenCL implementation of the Voigt and Hartmann-Tran lineshape functions. GAAS can be run on a variety of GPU hardware including integrated GPUs on most computers and high-performance external GPUs. It is installed as a standalone Python library, making it accessible and easy to use for many applications. GAAS will enable researchers to more efficiently analyze complex spectra, especially using advanced lineshapes, to ultimately increase the accuracy of complex spectroscopic measurements.

中文翻译：

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GAAS：GPU 加速吸收模拟器


解释测得的吸收光谱数据可能需要对预期的吸收光谱进行重复模拟，以拟合数据。在高温或宽带光谱的情况下，光谱分析的计算负载可能很昂贵，因为每次模拟都会有大量的单个吸收跃迁。我们推出了图形处理单元 （GPU） 加速吸收模拟器 （GAAS），这是一款快速、硬件加速的逐行吸收模拟软件，用于根据 Voigt 和 Hartmann-Tran 线形轮廓生成吸收光谱。我们表明，GAAS 产生的输出光谱与高分辨率透射分子吸收数据库 （HITRAN） 应用程序编程接口 （HAPI） 相同，基于 Voigt 和 Hartmann-Tran 剖面的光谱的数值精度在 32 位以内。我们还测量了与 HAPI 相比的性能提升，并证明 GAAS 可以将包含许多（数千或更多）吸收跃迁的光谱的模拟时间缩短多达 115 倍。该软件以开源 python 库的形式提供，该库是围绕 Voigt 和 Hartmann-Tran 线形函数的 OpenCL 实现构建的。GAAS 可以在各种 GPU 硬件上运行，包括大多数计算机上的集成 GPU 和高性能外部 GPU。它作为独立的 Python 库安装，使其易于访问和用于许多应用程序。GAAS 将使研究人员能够更有效地分析复杂光谱，尤其是使用高级线形，最终提高复杂光谱测量的准确性。