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.

行进/停留时间分布（TTD / RTD）是评估集水区易受污染并了解集水区功能和行为的许多方面的重要工具。近年来，基于拉格朗日粒子跟踪方法以及综合水文模型的TTD / RTD的计算已成为分析方法和集总数值模型的流行替代方法。由于人为干扰和气候变化使全球水资源供应更加紧张，对TTD / RTD进行大规模和长期模拟的需求进一步推高了拉格朗日粒子跟踪的高计算成本。因此，加快拉格朗日粒子跟踪方法成为发展的重要障碍。在这项研究中，我们使用分布式（例如MPI）和多核加速器（CUDA）方法的组合来进行大规模和长期模拟，从而加速了拉格朗日粒子跟踪程序EcoSLIM。EcoSLIM的开发是与集成的ParFlow.CLM模型无缝配对的，用于计算瞬时RTD和源水混合，并且最初是使用线程OpenMP开发的。这项工作扩展了该实现，以比较MPI，CUDA和OpenMP的组合。在这些组合中，OpenMP-CUDA并行性在从单GPU到多GPU的转换中表现最佳。多GPU显示出强大的可扩展性，随着更多粒子的出现，这种可扩展性变得越来越有效，这证明了在区域范围内进行瞬态停留时间模拟的潜在可行性。这项工作大大提高了EcoSLIM的计算能力，