GPU-native adaptive mesh refinement with application to lattice Boltzmann simulations
Bibliographic record
Abstract
Adaptive Mesh Refinement (AMR) enables efficient computation of flows by providing high resolution in critical regions while allowing for coarsening in areas where fine detail is unnecessary. While early AMR software packages relied solely on CPU parallelization, the widespread adoption of heterogeneous computing systems has led to GPU-accelerated implementations. In these hybrid approaches, simulation data typically resides on the GPU, and mesh management and adaptation occur exclusively on the CPU, necessitating frequent data transfers between them. A more efficient strategy is to adapt and maintain the entire mesh structure exclusively on the GPU, eliminating these transfers. Because of its inherent parallelism, the Lattice Boltzmann Method (LBM) has been widely implemented in hybrid AMR frameworks. This work presents a GPU-native algorithm for AMR using a block-based forest of octrees approach, implemented in both two and three dimensions as open-source C++/CUDA code. The implementation includes a Lattice Boltzmann solver for weakly compressible flow, though the underlying grid refinement procedure is compatible with any solver operating on cell-centered block-based grids. The lid-driven cavity and flow past a square cylinder benchmarks validate the algorithm's effectiveness across multiple velocity sets in both single- and double-precision. Tests conducted on consumer and datacenter-grade GPUs demonstrate its versatility across different hardware platforms. Link to repository: https://github.com/KhodrJ/AGAL .
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".