Lethe-DEM: an open-source parallel discrete element solver with load balancing
Bibliographic record
Abstract
Approximately $${75}\%$$ of the raw material and $${50}\%$$ of the products in the chemical industry are granular materials. The discrete element method (DEM) provides detailed insights of phenomena at particle scale, and it is therefore often used for modeling granular materials. However, because DEM tracks the motion and contact of individual particles separately, its computational cost increases nonlinearly $$O(n_\mathrm{p}\log (n_\mathrm{p}))$$ – $$O(n_\mathrm{p}^2)$$ (depending on the algorithm) with the number of particles ( $$n_\mathrm{p}$$ ). In this article, we introduce a new open-source parallel DEM software with load balancing: Lethe-DEM. Lethe-DEM, a module of Lethe, consists of solvers for two-dimensional and three-dimensional DEM simulations. Load balancing allows Lethe-DEM to significantly increase the parallel efficiency by $$\approx {25}$$ – $${70}\%$$ depending on the granular simulation. We explain the fundamental modules of Lethe-DEM, its software architecture, and the governing equations. Furthermore, we verify Lethe-DEM with several tests including analytical solutions and comparison with other software. Comparisons with experiments in a flat-bottomed silo, wedge-shaped silo, and rotating drum validate Lethe-DEM. We investigate the strong and weak scaling of Lethe-DEM with $${1}\le n_\mathrm{c} \le {192}$$ and $${32}\le n_\mathrm{c} \le {320}$$ processes, respectively, with and without load balancing. The strong-scaling analysis is performed on the wedge-shaped silo and rotating drum simulations, while for the weak-scaling analysis, we use a dam-break simulation. The best scalability of Lethe-DEM is obtained in the range of $${5000}\le n_\mathrm{p}/n_\mathrm{c} \le {15{,}000}$$ . Finally, we demonstrate that large-scale simulations can be carried out with Lethe-DEM using the simulation of a three-dimensional cylindrical silo with $$n_\mathrm{p}={4.3}\times 10^6$$ on 320 cores.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".