4.7 Article

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

期刊

COMPUTERS & GEOSCIENCES
卷 151, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cageo.2021.104760

关键词

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

资金

  1. National Natural Science Foundation of China (NSFC) [41807198]
  2. Strategic Priority Research Program of Chinese Academy of Sciences [XDA20100104]
  3. Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund
  4. U.S. Department of Energy Office of Science, Offices of Advanced Scientific Computing Research and Biological and Environmental Sciences IDEAS-Watersheds project
  5. U.S. National Science Foundation Cyber-Infrastructure project, HydroFrame (NSF-OAC) [1835903]
  6. U.S. National Science Foundation INFEWS-China [NSF-1805160]
  7. Center for Computational Science and Engineering of Southern University of Science and Technology
  8. National Supercomputer Center in Guangzhou, China
  9. Direct For Computer & Info Scie & Enginr
  10. Office of Advanced Cyberinfrastructure (OAC) [1835903] Funding Source: National Science Foundation

向作者/读者索取更多资源

This study accelerates the Lagrangian particle tracking program EcoSLIM using a combination of MPI and CUDA, improving its computational capability and showing the advantages of using GPU in subsurface environment modeling over traditional parallel APIs.
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 parallelAPIs (application programming interfaces) and its potential to widely accelerate the next generation programs in subsurface environment modeling.

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