An GPU-accelerated particle tracking method for Eulerian–Lagrangian simulations using hardware ray tracing cores

Wang, Bin; Wald, Ingo; Morrical, Nate; Usher, Will; Mu, Lin; Thompson, Karsten E.; Hughes, Richard G.

doi:10.1016/j.cpc.2021.108221

Cited by 12 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 33 developed the GPU parallel computing program for simulating transient thermal stress and welding deformation. The GPU-based parallel algorithm has been successfully used to improve the efficiency in fluid dynamics simulations, 34 , 35 , 36 Eulerian-Lagrangian simulations, 37 phase field simulations, 38 the combined discrete-finite element simulations, 39 the meshfree simulations, 40 , 41 etc. However, for the process EMF, the large deformation of workpiece and the electromagnetic field are intimately coupled, which are solved with the explicit method and implicit method, respectively.…”

Section: Introductionmentioning

confidence: 99%

High-efficiency computation for electromagnetic forming process: An explicit-implicit GPU approach

Pei,

Tang,

et al. 2024

iScience

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

High-efficiency computation for electromagnetic forming process: An explicit-implicit GPU approach

Pei,

Tang,

et al. 2024

iScience

View full text Add to dashboard Cite

“…(2019) and our previous work (C. Yang, Zhang, et al., 2021; C. Yang et al., 2022) leveraged the heterogeneous parallel architecture of multi‐GPU (Graphics Processing Unit) with OpenMP or MPI. GPU acceleration is growing in hydrologic models, including some particle tracking models (Hokkanen et al., 2021; Ji et al., 2014; Morales‐Hernandez et al., 2021; Rizzo et al., 2019; Wang et al., 2022), and in GCMs/ESMs (Fuhrer et al., 2018; Leutwyler et al., 2016) in recent years. This brings new opportunities to pursue faster speed of particle tracking models in Earth System Science but also induces more technical requirements to build an efficient parallel framework handling multi‐GPU.…”

Section: Introductionmentioning

confidence: 99%

Accelerating the Lagrangian Particle Tracking in Hydrologic Modeling to Continental‐Scale

Yang,

Ponder,

Wang

et al. 2023

J Adv Model Earth Syst

View full text Add to dashboard Cite

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.

show abstract

“…Along this line, Wald from NVIDIA and his collaborators first explored the capability of RT cores for locating points in a tetrahedron mesh. 26 Wang et al 27 utilized RT cores to trace particles in unstructured meshes for fluid flow in porous media. Zellmann et al 28 used RT cores to accelerate force-directed graph drawing.…”

Section: Introductionmentioning

confidence: 99%

Leveraging ray tracing cores for particle‐based simulations on GPUs

Zhao

Lai

Zhao

2022

Numerical Meth Engineering

View full text Add to dashboard Cite

Neighbor searching is an essential and computationally heavy step in particle‐based numerical methods such as discrete element method (DEM), molecular dynamics, peridynamics, and smooth particle hydrodynamics. This article presents a novel approach to accelerate particle‐based simulations by leveraging ray tracing (RT) cores in addition to CUDA cores on RTX GPUs. The neighbor search problem is first numerically converted into a general ray tracing problem so that it can be possible to utilize the hardware acceleration of RT cores. A new, general‐purpose RT‐based neighbor search algorithm is then proposed and benchmarked with a prevailing cell‐based one. As a showcase, both algorithms are implemented into a GPU‐based DEM code for simulating large‐scale granular problems including packing, column collapse and debris flow. The overall simulation performance is examined with varying problem sizes and GPU specs. It demonstrates that the RT‐based simulations are 10%–60% faster than the cell‐based ones, depending on the simulated problems and GPU specs. This study offers a new recipe for next‐generation high‐performance computing of large‐scale engineering problems using particle‐based numerical methods.

show abstract

An GPU-accelerated particle tracking method for Eulerian–Lagrangian simulations using hardware ray tracing cores

Cited by 12 publications

References 25 publications

High-efficiency computation for electromagnetic forming process: An explicit-implicit GPU approach

High-efficiency computation for electromagnetic forming process: An explicit-implicit GPU approach

Accelerating the Lagrangian Particle Tracking in Hydrologic Modeling to Continental‐Scale

Leveraging ray tracing cores for particle‐based simulations on GPUs

Contact Info

Product

Resources

About