Computational Methods and GPU Acceleration in Plasma Physics: A Empirical Analysis of arXiv Publications and Research Trends

Abstract

Computational plasma physics increasingly relies on high-performance computing (HPC) methods, including particle-in-cell (PIC), gyrokinetic, and magnetohydrodynamic (MHD) simulations, yet field-wide evidence on how method choice, team size, and GPU adoption shape research outputs remains limited. We analyze 5,522 computational plasma physics papers published on arXiv between 2010 and 2025 using large-scale text mining, employing abstract length as a proxy for methodological and algorithmic complexity. Using ordinary least squares (OLS), tobit, and logistic regression models, we examine how abstract length and GPU mentions vary with computational method, number of authors, and publication year. Controlling for collaboration size and temporal trends, results show that MHD studies have longer abstracts than PIC and gyrokinetic papers, indicating more extensive methodological and physical exposition. Abstract length increases modestly with team size, while temporal effects suggest gradual changes in abstract conciseness over time. At the same time, PIC and gyrokinetic methods have grown substantially in relative prevalence over the past decade and are strongly linked to GPU adoption, reflecting their higher computational intensity and suitability for accelerator-based architectures. Together, these findings highlight a decoupling between methodological verbosity and method prevalence, offering a new bibliometric perspective on the evolution of HPC-driven plasma physics research.