Load-Balancing Strategies in Discrete Element Method Simulations
Bibliographic record
Abstract
In this research, we investigate the influence of a load-balancing strategy and parametrization on the speed-up of discrete element method simulations using Lethe-DEM. Lethe-DEM is an open-source DEM code which uses a cell-based load-balancing strategy. We compare the computational performance of different cell-weighing strategies based on the number of particles per cell (linear and quadratic). We observe two minimums for particle to cell weights (at 3, 40 for quadratic, and 15, 50 for linear) in both linear and quadratic strategies. The first and second minimums are attributed to the suitable distribution of cell-based and particle-based functions, respectively. We use four benchmark simulations (packing, rotating drum, silo, and V blender) to investigate the computational performances of different load-balancing schemes (namely, single-step, frequent and dynamic). These benchmarks are chosen to demonstrate different scenarios that may occur in a DEM simulation. In a large-scale rotating drum simulation, which shows the systems in which particles occupy a constant region after reaching steady-state, single-step load-balancing shows the best performance. In a silo and V blender, where particles move in one direction or have a reciprocating motion, frequent and dynamic schemes are preferred. We propose an automatic load-balancing scheme (dynamic) that finds the best load-balancing steps according to the imbalance of computational load between the processes. Furthermore, we show the high computational performance of Lethe-DEM in the simulation of the packing of 108 particles on 4800 processes. We show that simulations with optimum load-balancing need ≈40% less time compared to the simulations with no load-balancing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".