LEDDS: Portable LBM-DEM simulations on GPUs

TL;DR

LEDDS demonstrates a portable, high-performance GPU framework for complex physics simulations using algorithmic primitives, enabling efficient LBM-DEM modeling across diverse hardware.

Contribution

It introduces LEDDS, an open-source, primitive-based GPU framework for coupled LBM-DEM simulations, emphasizing portability and performance.

Findings

Achieves performance comparable to hand-tuned CUDA solvers.

Validates simulations across various physics benchmarks.

Maintains high code clarity and portability.

Abstract

Algorithmic formulations of GPU programs provide a high-level alternative to device-specific code by expressing computations as compositions of well-defined parallel primitives (e.g., map, sort, reduce), rather than through handcrafted GPU kernels. In this work, we demonstrate that this paradigm can be extended to complex and challenging problems in computational physics: the simulation of granular flows and fluid-particle interactions. LEDDS, our open-source framework, performs fully coupled Lattice Boltzmann -- Discrete Element Method (LBM-DEM) simulations using only algorithmic primitives, and runs efficiently on single-GPU platforms. The entire workflow, including neighbor search, collision detection, and fluid-particle coupling, is expressed as a sequence of portable primitives. While the current implementation illustrates these principles primarily through algorithms from the C++ Standard Library, with selective use of Thrust primitives for performance, the underlying concept is compatible with any HPC environment offering a rich set of parallel algorithms and is therefore applicable across a wide range of modern GPU systems and future accelerators. LEDDS is validated through benchmarks spanning both DEM and LBM-DEM configurations, including sphere and ellipsoid collisions, wall friction tests, single-particle settling, Jeffery's orbits, and particle-laden shear flows. Despite its high level of abstraction, LEDDS achieves performances comparable to those of hand-tuned CUDA solvers, while maintaining portability and code clarity. These results show that high-performance LBM-DEM coupling can be achieved without sacrificing generality or readability, establishing LEDDS as a blueprint for portable multiphysics frameworks based on algorithmic primitives.