Scalable Stellar Parameter Inference Using Python-based LASP: From CPU Optimization to GPU Acceleration

作者：Liang Jun-chao, Yin-bi Li, A-Li Luo, Fang Zuo, Bing Du, Shuo Li, Xiao-Xiao Ma, Shu-Guo Ma, Hai-Ling Lu, WU Ke-fei, Zhi-Hua Zhong, Wen Hou, Xiao Kong, Shuo Ye, Li-Li Wang, H. R. A. Jones · 发表于：The Astrophysical Journal · 年份：2025 · DOI：10.3847/1538-4357/ae1446 · 被引用次数：1 · 研究领域：Stellar, planetary, and galactic studies、Astronomy and Astrophysical Research、Galaxies: Formation, Evolution, Phenomena

Abstract To enhance the efficiency, scalability, and cross-survey applicability of stellar parameter inference in large spectroscopic datasets, we present a modular, parallelized Python framework with automated error estimation, built on the LAMOST Atmospheric Parameter Pipeline (LASP) originally implemented in IDL. Rather than a direct code translation, this framework refactors LASP with two complementary modules: LASP-CurveFit, a new implementation of the LASP fitting procedure that runs on a CPU, preserving legacy logic while improving data I/O and multithreaded execution efficiency; and LASP-Adam-GPU, a GPU-accelerated method that introduces grouped optimization by constructing a joint residual function over multiple observed and model spectra, enabling high-throughput parameter inference across tens of millions of spectra. Applied to 10 million LAMOST spectra, the framework reduces runtime from 84 to 48 hr on the same CPU platform and to 7 hr on an NVIDIA A100 GPU, while producing results consistent with those from the original pipeline. The inferred errors agree well with the parameter variations from repeat observations of the same target (excluding radial velocities), while the official empirical errors used in LASP are more conservative. When applied to DESI DR1, our effective temperatures and surface gravities agree better with APOGEE than those from the DESI pipeline, particularly for cool giants, while the latter performs slightly better in radial velocity and met...