Massively parallel tandem traversal of bounding volume hierarchies for geometric queries on distributed meshes
Bibliographic record
Abstract
The efficient computation of geometric queries on large sets of elements, such as intersections or nearest neighbor searches, is crucial for many applications such as contact detection in mechanics simulations. While hierarchical data structures based on spatial or geometric partitioning have been extensively studied over the past 40 years, research detailing their adaptation to massively distributed environments using the message passing interface library—specifically how the communications are done and optimized for parallel efficiency—remains under-explored. We quantify the impact of various design choices to achieve rapid contact detection in massively distributed simulations using bounding volume hierarchies. The memory layout is optimized by reducing the hierarchy node size and ordering them with an innovative “L-filling curve” pattern. Next, the impact of a tandem traversal algorithm is analyzed, along with the optimal exploration order of both hierarchies’ nodes. The parallelization of this tandem traversal is presented, focusing on balancing message latency and volume to enhance parallel efficiency. These design choices extend to a wide range of applications and data structures. Finally, the algorithm’s strong performance is demonstrated up to 20,000 MPI tasks to handle tens of billions of intersections on very large meshes exceeding 100 billion elements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".