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The authors present a refactored and optimized Python framework, built upon the LAMOST Atmospheric Parameter Pipeline (LASP), for scalable stellar parameter inference from large spectroscopic datasets. The framework includes a CPU-optimized module (LASP-CurveFit) and a GPU-accelerated module (LASP-Adam-GPU) that uses grouped optimization to process multiple spectra simultaneously. Applied to 10 million LAMOST spectra, the framework achieves significant speedups (reducing runtime to 7 hours on an NVIDIA A100 GPU) while maintaining accuracy and demonstrating improved transferability to the DESI DR1 dataset compared to the DESI pipeline, particularly for effective temperature and surface gravity of cool giants.
Stellar parameter inference just got a whole lot faster: a new Python framework slashes runtime from 84 hours to just 7 using GPU acceleration while improving accuracy on cool giants.
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 metallicity. These results suggest that the framework delivers reliable accuracy, efficiency, and transferability, offering a practical approach to parameter inference in large spectroscopic surveys. The code and DESI-based catalog are available via DOI: 10.12149/101679 and DOI: 10.12149/101675, respectively.