Accelerating the Lagrangian particle tracking of residence time distributions and source water mixing towards large scales

Yang，Chen1; Zhang，You Kuan3; Liang，Xiuyu3; Olschanowsky，Catherine4; Yang，Xiaofan5; Maxwell，Reed1,2

2021-06-01

10.1016/j.cageo.2021.104760

COMPUTERS & GEOSCIENCES   影响因子和分区

0098-3004

1873-7803

Travel/residence time distributions (TTDs/RTDs) are important tools to evaluate the vulnerability of catchments to contamination and understand many aspects of catchment function and behavior. In recent years, the calculation of TTDs/RTDs based on the Lagrangian particle tracking approach together with the integrated hydrologic modeling has become a popular counterpart to analytical approaches and lumped numerical models. As global water availability becomes more stressed due to anthropogenic disturbance and climate change, the requirement of large-scale and long-term simulations for TTDs/RTDs further pushes the high computational costs of Lagrangian particle tracking. Hence, speeding up the Lagrangian particle tracking approach becomes an important barrier to advancement. In this study, we accelerate the Lagrangian particle tracking program EcoSLIM, using a combination of distributed (e.g. MPI) and manycore accelerator (CUDA) approaches for large-scale and long-term simulations. EcoSLIM was developed to be seamlessly paired with the integrated ParFlow.CLM model for calculations of transient RTDs and source water mixing and was originally developed using threaded OpenMP. This work extends this implementation to compare combinations of MPI, CUDA and OpenMP. Of these combinations, the OpenMP-CUDA parallelism performed the best moving from single-GPU to multi-GPU. The multi-GPU shows strong scalability which becomes increasingly efficient with more particles, demonstrating a potential feasibility for regional-scale, transient residence time simulations. This work largely improves the computational capability of EcoSLIM, and results also show the advantages of using GPU to traditional parallel-APIs (application programming interfaces) and its potential to widely accelerate the next generation programs in subsurface environment modeling.

Integrated modeling Lagrangian particle tracking MPI Multi-GPU Travel/residence time distributions

SCI ; EI

英语

其他

National Natural Science Foundation of China (NSFC)[41807198] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA20100104] ; U.S. National Science Foundation Cyber-Infrastructure project, HydroFrame (NSF-OAC)[1835903] ; U.S. National Science Foundation INFEWS-China[NSF-1805160]

Computer Science ; Geology

Computer Science, Interdisciplinary Applications ; Geosciences, Multidisciplinary

WOS:000641463100004

PERGAMON-ELSEVIER SCIENCE LTD

20211510193752

Application programming interfaces (API) ; Application programs ; Catchments ; Climate change ; Lagrange multipliers ; Mixing ; Runoff

Flood Control:442.1 ; Atmospheric Properties:443.1 ; Computer Software, Data Handling and Applications:723 ; Chemical Operations:802.3 ; Probability Theory:922.1

COMPUTER SCIENCE

2-s2.0-85103687184

Scopus

1.Department of Civil and Environmental Engineering,Princeton University,Princeton,United States
2.High Meadows Environmental Institute,Princeton University,Princeton,United States
3.School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
4.Computer Science Department,College of Engineering,Boise State University,Boise,83704,United States
5.State Key Laboratory of Earth Surface Processes and Resource Ecology,Faculty of Geographical Science,Beijing Normal University,Beijing,100875,China

Yang，Chen,Zhang，You Kuan,Liang，Xiuyu,et al. Accelerating the Lagrangian particle tracking of residence time distributions and source water mixing towards large scales[J]. COMPUTERS & GEOSCIENCES,2021,151.

Yang，Chen,Zhang，You Kuan,Liang，Xiuyu,Olschanowsky，Catherine,Yang，Xiaofan,&Maxwell，Reed.(2021).Accelerating the Lagrangian particle tracking of residence time distributions and source water mixing towards large scales.COMPUTERS & GEOSCIENCES,151.

Yang，Chen,et al."Accelerating the Lagrangian particle tracking of residence time distributions and source water mixing towards large scales".COMPUTERS & GEOSCIENCES 151(2021).