Parallelization of the streamline simulation based on CUDA

To accelerate the streamline simulation and satisfy the real-time demands, in this paper, we proposed a method based on GPUs to parallelize the streamline simulation. CUDA architecture was used to implement the parallel algorithm on a single GPU and a multi-GPU computer. In our method, a grid is organized into a 2D array of blocks, and all threads in a block are organized into a 1D array, such that each thread in a block computes one streamline. To implement the method on multiple GPUs, the physical cell model is divided into sub-models to make the number of sub-models equal to the number of GPUs. The algorithm is applied to a Tóthian basin as an example. The experimental analysis shows that the parallel algorithm based on different numbers of GPUs has different accelerations. For a single GPU, the speedup reaches 170 times; and for five GPUs, it is 808 times, for a physical model with 40×106 cells. The conclusion is that GPUs can greatly accelerate the streamline simulation.

[1]  Ping Wang,et al.  Parallel Computation and Visualization of 3D, Time-Dependent, Thermal Convective Flows , 1998, PARA.

[2]  Margot Gerritsen,et al.  Parallel implementations of streamline simulators , 2009 .

[3]  Salam Jabbar Hussain Al Rbeawi,et al.  Effect of the Number and Length of Zonal Isolations on Pressure Behavior of Horizontal Wells , 2011 .

[4]  Majid Siavashi,et al.  Efficient Particle Swarm Optimization of Well Placement to Enhance Oil Recovery Using a Novel Streamline-Based Objective Function , 2016 .

[5]  Thomas Ertl,et al.  GPU-Based Streamlines for Surface-Guided 3D Flow Visualization , 2008, VMV.

[6]  David W. Pollock,et al.  User Guide for MODPATH Version 6-A Particle-Tracking Model for MODFLOW , 2014 .

[7]  Wen-mei W. Hwu,et al.  Programming Massively Parallel Processors, Third Edition: A Hands-on Approach , 2016 .

[8]  Tolga Soyata GPU Parallel Program Development Using CUDA , 2018 .

[9]  D. W. Pollock Semianalytical Computation of Path Lines for Finite‐Difference Models , 1988 .

[10]  Denis José Schiozer,et al.  Compressible Streamline-Based Simulation With Changes in Oil Composition , 2009 .

[11]  P. D. Sreedevi,et al.  Governing Equations of Groundwater Flow and Aquifer Modelling Using Finite Difference Method , 2008 .

[12]  Jeffrey S. Vetter,et al.  A Survey of CPU-GPU Heterogeneous Computing Techniques , 2015, ACM Comput. Surv..

[13]  Helmut Pralle,et al.  Using Streaming and Parallelization Techniques for 3D Visualization in a High-Performance Computing and Networking Environment , 2001, HPCN Europe.

[14]  Kishore K. Mohanty,et al.  Compositional Streamline Simulation: A Parallel Implementation , 2011 .

[15]  Robert B. Ross,et al.  A Study of Parallel Particle Tracing for Steady-State and Time-Varying Flow Fields , 2011, 2011 IEEE International Parallel & Distributed Processing Symposium.

[16]  Xiaohui Ji,et al.  Identifying three-dimensional nested groundwater flow systems in a Tóthian basin , 2017 .

[17]  Wan Nor Azmin Sulaiman,et al.  Particle tracking analysis of river–aquifer interaction via bank infiltration techniques , 2014, Environmental Earth Sciences.

[18]  F. Bratvedt,et al.  Streamline computations for porous media flow including gravity , 1996 .

[19]  Sandow Mark Yidana,et al.  Groundwater flow modeling and particle tracking for chemical transport in the southern Voltaian aquifers , 2011 .