Virtual LiDAR Simulation as a High Performance Computing Challenge: Toward HPC HELIOS++
Bibliographic record
Abstract
The software HELIOS++ simulates the laser scanning of a given virtual scene that can be composed of different spatial primitives and 3D meshes with distinct granularity. The high computational cost of this type of simulation software demands efficient computational solutions. Classical solutions based on GPU are not well suited when irregular geometries compose the scene combining different primitives and physics models because they lead to different computation branches. In this paper, we explore the usage of parallelization strategies based on static and dynamic workload balancing and heuristic optimization strategies to speed up the ray tracing process based on a k-dimensional tree (KDT). Using HELIOS++ as our case study, we analyze the performance of our algorithms on different parallel computers, including the CESGA FinisTerrae-II supercomputer. There is a significant performance boost in all cases, with the decrease in computation time ranging from 89.5% to 99.4%. Our results show that the proposed algorithms can boost the performance of any software that relies heavily on a KDT or a similar data structure, as well as those that spend most of the time computing with only a few synchronization barriers. Hence, the algorithms presented in this paper improve performance, whether computed on personal computers or supercomputers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".