Communication analysis and optimization of 3D front tracking method for multiphase flow simulations
Bibliographic record
Abstract
This paper presents a scalable parallelization of an Eulerian–Lagrangian method, namely the three-dimensional front tracking method, for simulating multiphase flows. Operating on Eulerian–Lagrangian grids makes the front tracking method challenging to parallelize and optimize because different types of communication (Lagrangian–Eulerian, Eulerian–Eulerian, and Lagrangian–Lagrangian) should be managed. In this work, we optimize the data movement in both the Eulerian and Lagrangian grids and propose two different strategies for handling the Lagrangian grid shared by multiple subdomains. Moreover, we model three different types of communication emerged as a result of parallelization and implement various latency-hiding optimizations to reduce the communication overhead. Good scalability of the parallelization strategies is demonstrated on two supercomputers. A strong scaling study using 256 cores simulating 1728 interfaces or bubbles achieves 32.5x speedup. We also conduct weak scaling study on 4096 cores simulating 27,648 bubbles on a 1024×1024×2048 Eulerian grid resolution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".