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arxiv: 2512.04997 · v2 · submitted 2025-12-04 · ⚛️ physics.comp-ph

LEDDS: Portable LBM-DEM simulations on GPUs

Raphael Maggio-Aprile , Maxime Rambosson , Christophe Coreixas , Jonas Latt This is my paper

Pith reviewed 2026-05-17 00:51 UTC · model grok-4.3

classification ⚛️ physics.comp-ph
keywords LBM-DEMGPU simulationsportable computingparallel primitivesgranular flowsfluid-particle couplingLattice Boltzmann MethodDiscrete Element Method
0
0 comments X

The pith

LBM-DEM simulations achieve hand-tuned CUDA performance using only portable parallel primitives on GPUs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper introduces LEDDS as a framework for fully coupled Lattice Boltzmann and Discrete Element Method simulations on single-GPU platforms. It expresses the complete workflow, including neighbor search, collision detection, and fluid-particle coupling, as sequences of portable parallel primitives drawn from the C++ Standard Library and Thrust. The approach is validated across DEM and LBM-DEM benchmarks such as sphere and ellipsoid collisions, single-particle settling, Jeffery's orbits, and particle-laden shear flows. If correct, the work shows that high-level algorithmic formulations can deliver performance comparable to custom low-level code while preserving portability and readability. This would matter because it offers a route to maintainable multiphysics codes that transfer across GPU systems without repeated device-specific rewrites.

Core claim

LEDDS demonstrates that fully coupled LBM-DEM simulations can be performed by composing the entire workflow from algorithmic primitives rather than handcrafted GPU kernels. The full process of neighbor search, collision detection, and fluid-particle coupling is expressed as portable operations, and the resulting code runs efficiently on single GPUs. Benchmarks confirm that this high-abstraction implementation matches the performance of hand-tuned CUDA solvers while remaining portable to other HPC environments and keeping the source code clear.

What carries the argument

Algorithmic formulations that express computations as compositions of well-defined parallel primitives such as map, sort, and reduce, applied to the full LBM-DEM workflow.

If this is right

  • Performance comparable to hand-tuned CUDA solvers is achieved despite the high level of abstraction.
  • The framework maintains portability across a wide range of modern GPU systems and future accelerators.
  • Code clarity is preserved while delivering high performance for complex fluid-particle simulations.
  • LEDDS serves as a blueprint for portable multiphysics frameworks based on algorithmic primitives.
  • The method applies to both pure DEM and coupled LBM-DEM configurations across the listed validation cases.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Developers without specialized GPU programming expertise could more readily implement or modify such simulations.
  • The primitive-based abstraction might extend to additional multiphysics couplings such as fluid-structure interactions.
  • Standard libraries of parallel primitives could become a common foundation for scientific HPC codes.
  • Long-term maintenance costs for simulation software may decrease as hardware changes require fewer code rewrites.

Load-bearing premise

That expressing neighbor search, collision detection, and fluid-particle coupling entirely through portable parallel primitives introduces no significant performance penalty or accuracy loss across the range of DEM and LBM-DEM benchmarks considered.

What would settle it

A side-by-side run of one of the paper's benchmarks, such as particle-laden shear flow or single-particle settling, on identical hardware where the LEDDS version runs substantially slower or produces measurably different physical quantities than a hand-tuned CUDA implementation.

Figures

Figures reproduced from arXiv: 2512.04997 by Christophe Coreixas, Jonas Latt, Maxime Rambosson, Raphael Maggio-Aprile.

Figure 1
Figure 1. Figure 1: 2D schematic of the coupled particle-fluid simulation. Bold lines [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Intersection plane which characterizes a collision between two [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the LEDDS workflow from the particle perspective. Col [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of the 2D uniform grid. Spheres [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Three-step procedure for the global computation of all unique collision pairs, illustrated with an example (solids are represented by letters instead of [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The particle-particle forces are computed during a parallel map operation applied to all particles. For the force acting on a given particle, a non-parallel [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Head-on elastic collision: Time evolution of the spheres’ longitudinal [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Head-on elastic collision: Time evolution of total [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: Setup of the simulation for the test. Before the collision, the sphere is [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Evolution of the post-collision velocity angle (tan [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Many-body validation: time evolution of the particle velocity distribution ( [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Single particle settling simulation: Time evolution of the settling [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
Figure 15
Figure 15. Figure 15: Shear flow simulations: Instantaneous particle configurations for [PITH_FULL_IMAGE:figures/full_fig_p017_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Evolution of the apparent viscosity (η/η0) as a function of the volume fraction (ϕ). Simulation data (symbols) are compared to the Krieger￾Dougherty semi-analytical law [73]. 5. Performance analysis To assess the computational efficiency and scalability of LEDDS, we perform a series of performance benchmarks us￾ing a fluidized-bed configuration across a range of solid vol￾ume fractions. The analysis is co… view at source ↗
Figure 17
Figure 17. Figure 17: CPU performance comparison of single (FP32) and double (FP64) [PITH_FULL_IMAGE:figures/full_fig_p019_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: GPU performance comparison of a pure C++ STL implementa￾tion against an accelerated version, using the reduce_by_key algorithm and a radix-sort based version of sort from the Thrust library. Both implementations use double precision (FP64). 5.4. GPU performance Continuing with the GPU performance analysis of LEDDS, we first compare the use of STL algorithms and Thrust primi￾tives for the DEM and PSM-based… view at source ↗
Figure 19
Figure 19. Figure 19: GPU performance of the STL/Thrust version of LEDDS in sin￾gle (FP32) and double precision (FP64) across varying solid volume fractions. Both set of data were generated with the STL/Thrust version of LEDDS. double precision (FP64). In dilute configurations (ϕ = 0), where the solver is predominantly memory-bound due to the LBM updates, switching from FP64 to FP32 yields a significant performance gain simila… view at source ↗
Figure 21
Figure 21. Figure 21: GPU performance comparison between LEDDS and waLBerla [PITH_FULL_IMAGE:figures/full_fig_p021_21.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript introduces LEDDS, an open-source framework for fully coupled LBM-DEM simulations on single-GPU platforms. All components—neighbor search, collision detection, and fluid-particle coupling—are expressed exclusively as compositions of portable parallel primitives drawn from the C++ Standard Library with selective Thrust usage. The work validates the approach on DEM and LBM-DEM benchmarks (sphere/ellipsoid collisions, wall friction, single-particle settling, Jeffery orbits, particle-laden shear flow) and claims that the resulting performance remains comparable to hand-tuned CUDA implementations while preserving portability and readability.

Significance. If the reported benchmark outcomes hold, the result is significant: it supplies a concrete, reproducible demonstration that complex multiphysics codes can be written at a high level of algorithmic abstraction without incurring prohibitive performance penalties. The open-source release, the explicit mapping of physical operations onto standard parallel primitives, and the absence of device-specific kernels constitute clear strengths that could serve as a template for portable HPC frameworks on current and future accelerators.

minor comments (3)
  1. §4 (Implementation): the description of how the neighbor-search primitive is composed from std::sort and std::transform should include a short pseudocode listing or explicit call sequence so that readers can reproduce the mapping without inspecting the source repository.
  2. Table 2 (Performance comparison): the reported wall-clock times for the LBM-DEM cases should be accompanied by the corresponding grid sizes, particle counts, and number of time steps to allow direct scaling comparisons with the cited hand-tuned CUDA references.
  3. §5.3 (Jeffery orbits): the statement that the computed orbits 'match analytical solutions' would be strengthened by reporting the L2 error norm or maximum angular deviation rather than a qualitative visual comparison alone.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive and accurate summary of the LEDDS framework, its use of portable algorithmic primitives for LBM-DEM coupling, and the recommendation for minor revision. The significance assessment aligns with our goals of demonstrating high-level abstractions without prohibitive performance loss.

Circularity Check

0 steps flagged

No significant circularity

full rationale

This is an implementation and benchmarking paper with no mathematical derivations, fitted parameters, predictions, or first-principles results. The central claims rest on reported benchmark outcomes (sphere/ellipsoid collisions, Jeffery orbits, particle-laden flows) and direct performance comparisons between the primitive-based approach and hand-tuned CUDA. No step reduces by construction to its own inputs, and the work is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that standard-library parallel primitives can implement the full set of LBM-DEM operations (neighbor search, collision handling, coupling) with acceptable efficiency and without custom kernels.

axioms (1)
  • domain assumption Parallel primitives available in C++ Standard Library and Thrust are sufficient to express neighbor search, collision detection, and fluid-particle coupling at high performance on GPUs.
    This assumption underpins both the portability claim and the performance comparison to hand-tuned CUDA.

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Reference graph

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