@article{e6da99d5d6f342b498cd2f7223bf1a1b,
title = "Accelerating the Lagrangian Particle Tracking in Hydrologic Modeling to Continental-Scale",
abstract = "Unprecedented climate change and anthropogenic activities have induced increasing ecohydrological problems, motivating the development of large-scale hydrologic modeling for solutions. Water age/quality is as important as water quantity for understanding the terrestrial water cycle. However, scientific progress in tracking water parcels at large-scale with high spatiotemporal resolutions is far behind that in simulating water balance/quantity owing to the lack of powerful modeling tools. EcoSLIM is a particle tracking model working with ParFlow-CLM that couples integrated surface-subsurface hydrology with land surface processes. Here, we demonstrate a parallel framework on distributed, multi-Graphics Processing Unit platforms with Compute Unified Device Architecture-Aware Message Passing Interface for accelerating EcoSLIM to continental-scale. In tests from catchment-, to regional-, and then to continental-scale using 25-million to 1.6-billion particles, EcoSLIM shows significant speedup and excellent parallel performance. The parallel framework is portable to atmospheric and oceanic particle tracking models, where the parallelization is inadequate, and a standard parallel framework is also absent. The parallelized EcoSLIM is a promising tool to accelerate our understanding of the terrestrial water cycle and the upscaling of subsurface hydrology to Earth System Models.",
keywords = "CUDA-Aware MPI, EcoSLIM, continental scale, multi-GPU, particle tracking",
author = "Chen Yang and Carl Ponder and Bei Wang and Hoang Tran and Jun Zhang and Jackson Swilley and Laura Condon and Reed Maxwell",
note = "Funding Information: We thank the anonymous reviewers and associate editor whose comments substantially improved our manuscript. This work was supported by the National Natural Science Foundation of China (NSFC‐41807198). This work was also supported by the U.S. Department of Energy Office of Science, Offices of Advanced Scientific Computing Research and Biological and Environmental Sciences IDEAS project, and Watershed Function Scientific Focus Area under Award Number DE‐AC02‐05CH11231. The simulations presented in this article were performed on computational resources managed and supported by Princeton Research Computing, a consortium of groups including the Princeton Institute for Computational Science and Engineering (PICSciE), and the Office of Information Technology's High‐Performance Computing Center and Visualization Laboratory at Princeton University. Funding Information: We thank the anonymous reviewers and associate editor whose comments substantially improved our manuscript. This work was supported by the National Natural Science Foundation of China (NSFC-41807198). This work was also supported by the U.S. Department of Energy Office of Science, Offices of Advanced Scientific Computing Research and Biological and Environmental Sciences IDEAS project, and Watershed Function Scientific Focus Area under Award Number DE-AC02-05CH11231. The simulations presented in this article were performed on computational resources managed and supported by Princeton Research Computing, a consortium of groups including the Princeton Institute for Computational Science and Engineering (PICSciE), and the Office of Information Technology's High-Performance Computing Center and Visualization Laboratory at Princeton University. Publisher Copyright: {\textcopyright} 2023 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union.",
year = "2023",
month = may,
doi = "10.1029/2022MS003507",
language = "English (US)",
volume = "15",
journal = "Journal of Advances in Modeling Earth Systems",
issn = "1942-2466",
publisher = "American Geophysical Union",
number = "5",
}