Leveraging ray tracing cores for particle‐based simulations on GPUs

Abstract

AbstractNeighbor 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.