Simulation of large-scale multitarget tracking scenarios using GPUs
Bibliographic record
Abstract
The increased availability of Graphical Processing Units (GPUs) in personal computers has made parallel pro- gramming worthwhile, but not necessarily easier. This paper will take advantage of the power of a GPU, in conjunction with the Central Processing Unit (CPU), in order to simulate target trajectories for large-scale scenarios, such as wide-area maritime or ground surveillance. The idea is to simulate the motion of tens of thousands of targets using a GPU by formulating an optimization problem that maximizes the throughput. To do this, the proposed algorithm is provided with input data that describes how the targets are expected to behave, path information (e.g., roadmaps, shipping lanes), and available computational resources. Then, it is possible to break down the algorithm into parts that are done in the CPU versus those sent to the GPU. The ultimate goal is to compare processing times of the algorithm with a GPU in conjunction with a CPU to those of the standard algorithms running on the CPU alone. In this paper, the optimization formulation for utilizing the GPU, simulation results on scenarios with a large number of targets and conclusions are provided.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".